{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The goal of this project is to predict the Crude Oil prices. Monthly petroleum prices can be found at the [Energy Information Administration](https://www.eia.gov/dnav/pet/pet_pri_spt_s1_m.htm). Ever relevant, [Wikipedia](https://en.wikipedia.org/wiki/World_oil_market_chronology_from_2003) has a great write-up on recent trends in oil prices. Also, there is this [Times](http://content.time.com/time/business/article/0,8599,1859380,00.html) article on the spike and drop in 2008.\n",
    "\n",
    "### Introduction:\n",
    "\n",
    "Tthere are different types of crude oil – the thick, unprocessed liquid that drillers extract below the earth – and some are more desirable than others. Also, where the oil comes from also makes a difference. Because of these nuances, we need benchmarks to value the commodity based on its quality and location. Thus, Brent, WTI and Dubai/Oman serve this important purpose. Here, I use the most important WTI to demonstrate the world crude oil price changes.\n",
    "\n",
    "West Texas Intermediate (WTI) – WTI refers to oil extracted from wells in the U.S. and sent via pipeline to Cushing, Oklahoma. The product itself is very light and very sweet, making it ideal for gasoline refining, in particular. WTI continues to be the main benchmark for oil consumed in the United States. Brent Blend – Roughly two-thirds of all crude contracts around the world reference Brent Blend, making it the most widely used marker of all. These days, “Brent” actually refers to oil from four different fields in the North Sea: Brent, Forties, Oseberg and Ekofisk. Crude from this region is light and sweet, making them ideal for the refining of diesel fuel, gasoline. For more information see [Wikipedia](https://en.wikipedia.org/wiki/West_Texas_Intermediate) article. \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required packages\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "import quandl\n",
    "import math\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import pandas as pd\n",
    "import sklearn.linear_model\n",
    "import sklearn.metrics\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.pylab as pylab\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "params = {'legend.fontsize': 'xx-large',\n",
    "          'figure.figsize': (15, 10),\n",
    "         'axes.labelsize': 'xx-large',\n",
    "         'axes.titlesize':'xx-large',\n",
    "         'xtick.labelsize':'xx-large',\n",
    "         'ytick.labelsize':'xx-large'}\n",
    "pylab.rcParams.update(params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time Series\n",
    "<!-- requirement: images/ts_xval.png -->\n",
    "<!-- requirement: projects/timeseries-project -->\n",
    "\n",
    "Time series prediction or forecasting presents its own challenges which are different from machine-learning problems.  \n",
    "\n",
    "Let's first define our time-series as\n",
    "\n",
    "$$ \\{X_t, t = \\ldots, -1, 0, 1, \\ldots\\} \\, . $$\n",
    "\n",
    "In general, while we could use regression to make predictions, the goal of time series analysis is to take advantage of the temporal nature of the data to make more sophisticated models.\n",
    "\n",
    "**Why not regression** : In typical time series analysis, the depended variables are missing. For exmaple, a simple linear regression would take a form of \n",
    "\n",
    "$$ Y = \\beta_0 + \\beta_1 X_1 + ...$$\n",
    "\n",
    "where X_1, X_2 ... are unknown. All we know is Y as a function of time.\n",
    "\n",
    "Here are a few concepts related to time series.  We take $\\varepsilon \\sim N(0, \\sigma^2)$ to be i.i.d. normal errors.  \n",
    "1. **Stationarity**.  Informally, this means that the distribution of the $X_t$ is independent of time $t$.  Formally, a time-series is stationary if for all $k \\ge 0$ and $t$, the following two $k$-tuples have the same distribution:\n",
    "$$ (X_0,\\ldots,X_k) \\sim (X_t,\\ldots,X_{t+k}) $$\n",
    "1. **Drift**.  One reason a time-series might not be stationary is that it possess a drift.  For example, we know that prices tend to creep up with inflation.  Mathematically, we might represent the (log) prices as\n",
    "$$ X_t = \\mu t + \\varepsilon_t $$\n",
    "1. **Seasonality**.  Another reason a time-series might not be stationary is that it posseses a seasonal component.  For example, we know that the temperature increases in the summer and decreases in the winter.  A simple model of this might be\n",
    "$$ X_t = \\alpha \\sin(\\omega t) + \\beta \\cos(\\omega t)$$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Crudeoil price dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "oil = quandl.get(\"CHRIS/CME_CL1\", collapse = \"weekly\")  # This creates pandas data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Change</th>\n",
       "      <th>Settle</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Previous Day Open Interest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1983-04-03</th>\n",
       "      <td>29.40</td>\n",
       "      <td>29.60</td>\n",
       "      <td>29.25</td>\n",
       "      <td>29.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.29</td>\n",
       "      <td>521.0</td>\n",
       "      <td>523.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-04-10</th>\n",
       "      <td>30.65</td>\n",
       "      <td>30.65</td>\n",
       "      <td>30.25</td>\n",
       "      <td>30.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.38</td>\n",
       "      <td>365.0</td>\n",
       "      <td>651.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-04-17</th>\n",
       "      <td>30.55</td>\n",
       "      <td>30.60</td>\n",
       "      <td>30.35</td>\n",
       "      <td>30.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.48</td>\n",
       "      <td>295.0</td>\n",
       "      <td>908.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-04-24</th>\n",
       "      <td>30.62</td>\n",
       "      <td>30.85</td>\n",
       "      <td>30.62</td>\n",
       "      <td>30.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.75</td>\n",
       "      <td>307.0</td>\n",
       "      <td>1075.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-05-01</th>\n",
       "      <td>30.70</td>\n",
       "      <td>30.74</td>\n",
       "      <td>30.63</td>\n",
       "      <td>30.63</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.63</td>\n",
       "      <td>323.0</td>\n",
       "      <td>927.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low   Last  Change  Settle  Volume  \\\n",
       "Date                                                             \n",
       "1983-04-03  29.40  29.60  29.25  29.29     NaN   29.29   521.0   \n",
       "1983-04-10  30.65  30.65  30.25  30.38     NaN   30.38   365.0   \n",
       "1983-04-17  30.55  30.60  30.35  30.48     NaN   30.48   295.0   \n",
       "1983-04-24  30.62  30.85  30.62  30.75     NaN   30.75   307.0   \n",
       "1983-05-01  30.70  30.74  30.63  30.63     NaN   30.63   323.0   \n",
       "\n",
       "            Previous Day Open Interest  \n",
       "Date                                    \n",
       "1983-04-03                       523.0  \n",
       "1983-04-10                       651.0  \n",
       "1983-04-17                       908.0  \n",
       "1983-04-24                      1075.0  \n",
       "1983-05-01                       927.0  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oil.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Last</th>\n",
       "      <th>Change</th>\n",
       "      <th>Settle</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Previous Day Open Interest</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-06-11</th>\n",
       "      <td>45.71</td>\n",
       "      <td>46.18</td>\n",
       "      <td>45.27</td>\n",
       "      <td>45.90</td>\n",
       "      <td>0.19</td>\n",
       "      <td>45.83</td>\n",
       "      <td>712341.0</td>\n",
       "      <td>399576.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-18</th>\n",
       "      <td>44.25</td>\n",
       "      <td>44.94</td>\n",
       "      <td>44.24</td>\n",
       "      <td>44.68</td>\n",
       "      <td>0.28</td>\n",
       "      <td>44.74</td>\n",
       "      <td>187501.0</td>\n",
       "      <td>104865.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-25</th>\n",
       "      <td>42.80</td>\n",
       "      <td>43.20</td>\n",
       "      <td>42.53</td>\n",
       "      <td>43.17</td>\n",
       "      <td>0.27</td>\n",
       "      <td>43.01</td>\n",
       "      <td>689563.0</td>\n",
       "      <td>535511.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-02</th>\n",
       "      <td>44.89</td>\n",
       "      <td>46.35</td>\n",
       "      <td>44.88</td>\n",
       "      <td>46.33</td>\n",
       "      <td>1.11</td>\n",
       "      <td>46.04</td>\n",
       "      <td>674176.0</td>\n",
       "      <td>519676.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-09</th>\n",
       "      <td>45.35</td>\n",
       "      <td>45.42</td>\n",
       "      <td>43.78</td>\n",
       "      <td>44.33</td>\n",
       "      <td>1.29</td>\n",
       "      <td>44.23</td>\n",
       "      <td>958132.0</td>\n",
       "      <td>448574.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "             Open   High    Low   Last  Change  Settle    Volume  \\\n",
       "Date                                                               \n",
       "2017-06-11  45.71  46.18  45.27  45.90    0.19   45.83  712341.0   \n",
       "2017-06-18  44.25  44.94  44.24  44.68    0.28   44.74  187501.0   \n",
       "2017-06-25  42.80  43.20  42.53  43.17    0.27   43.01  689563.0   \n",
       "2017-07-02  44.89  46.35  44.88  46.33    1.11   46.04  674176.0   \n",
       "2017-07-09  45.35  45.42  43.78  44.33    1.29   44.23  958132.0   \n",
       "\n",
       "            Previous Day Open Interest  \n",
       "Date                                    \n",
       "2017-06-11                    399576.0  \n",
       "2017-06-18                    104865.0  \n",
       "2017-06-25                    535511.0  \n",
       "2017-07-02                    519676.0  \n",
       "2017-07-09                    448574.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oil.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1267ddbd0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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d7ds5L0nat3Ou5r7JyNdCZ/oV7r6gYKDKyyO3Xy/pkKS7yu9/TtJVmUzmQnOH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1267ef610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "oil.Open.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 29.39999962,  30.64999962,  30.54999924, ...,  42.79999924,\n",
       "        44.88999939,  45.34999847], dtype=float32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setting the random seed\n",
    "\n",
    "np.random.seed(7)\n",
    "dataset_noscale = oil.Open.values\n",
    "dataset_noscale = dataset.astype('float32')\n",
    "dataset_noscale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sarthakdasadia/anaconda/lib/python2.7/site-packages/sklearn/preprocessing/data.py:321: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n",
      "/Users/sarthakdasadia/anaconda/lib/python2.7/site-packages/sklearn/preprocessing/data.py:356: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.13937622,  0.14874792,  0.14799818, ...,  0.23984104,\n",
       "        0.25551057,  0.25895935], dtype=float32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalize the dataset\n",
    "\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "dataset = scaler.fit_transform(dataset_noscale)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1252, 537)\n"
     ]
    }
   ],
   "source": [
    "# Split the datser into test and training data\n",
    "\n",
    "train_size = int(len(dataset) * 0.7)\n",
    "test_size = len(dataset) - train_size\n",
    "train, test = dataset[0:train_size], dataset[train_size:len(dataset)]\n",
    "print(len(train), len(test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write a simple function to convert single column of data into a two-column dataset. The first column containing today's (t) price and the second column containing next day’s (t+1) price and so on, to be predicted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# convert an array of values into a dataset matrix\n",
    "# look_back = number of features. Use ACF and PACF to determine this\n",
    "\n",
    "def create_dataset(dataset, look_back=1):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset)-look_back):\n",
    "        a = dataset[i:(i+look_back)]\n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[i + look_back])\n",
    "    return np.array(dataX), np.array(dataY)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this problem, I inspected ACF and PACF to determine look_back ~ 8."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Long Short-Term Memory (LSTM) Recurrent Network for Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Long Short-Term Memory network, or LSTM network, is a recurrent neural network that is trained using Backpropagation Through Time and overcomes the vanishing gradient problem.\n",
    "\n",
    "As such, it can be used to create large recurrent networks that in turn can be used to address difficult sequence problems in machine learning and achieve state-of-the-art results.\n",
    "\n",
    "Instead of neurons, LSTM networks have memory blocks that are connected through layers.\n",
    "\n",
    "A block has components that make it smarter than a classical neuron and a memory for recent sequences. A block contains gates that manage the block’s state and output. A block operates upon an input sequence and each gate within a block uses the sigmoid activation units to control whether they are triggered or not, making the change of state and addition of information flowing through the block conditional."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The network has a visible layer with 1 input, a hidden layer with 4 LSTM blocks or neurons, and an output layer that makes a single value prediction. The default sigmoid activation function is used for the LSTM blocks. The network is trained for 100 epochs and a batch size of 1 is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# reshape into X=t and Y=t+1\n",
    "\n",
    "look_back = 8\n",
    "trainX, trainY = create_dataset(train, look_back)\n",
    "testX, testY = create_dataset(test, look_back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1244, 8)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainX.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# reshape input to be [samples, time steps, features]\n",
    "\n",
    "trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\n",
    "testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1244, 1, 8)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainX.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "6s - loss: 0.0017\n",
      "Epoch 2/20\n",
      "5s - loss: 3.1096e-04\n",
      "Epoch 3/20\n",
      "5s - loss: 2.6509e-04\n",
      "Epoch 4/20\n",
      "5s - loss: 2.3709e-04\n",
      "Epoch 5/20\n",
      "5s - loss: 1.9690e-04\n",
      "Epoch 6/20\n",
      "5s - loss: 1.7884e-04\n",
      "Epoch 7/20\n",
      "5s - loss: 1.6227e-04\n",
      "Epoch 8/20\n",
      "5s - loss: 1.4887e-04\n",
      "Epoch 9/20\n",
      "5s - loss: 1.4622e-04\n",
      "Epoch 10/20\n",
      "5s - loss: 1.5449e-04\n",
      "Epoch 11/20\n",
      "5s - loss: 1.4629e-04\n",
      "Epoch 12/20\n",
      "5s - loss: 1.4187e-04\n",
      "Epoch 13/20\n",
      "5s - loss: 1.4038e-04\n",
      "Epoch 14/20\n",
      "5s - loss: 1.3625e-04\n",
      "Epoch 15/20\n",
      "5s - loss: 1.3060e-04\n",
      "Epoch 16/20\n",
      "5s - loss: 1.3906e-04\n",
      "Epoch 17/20\n",
      "5s - loss: 1.4102e-04\n",
      "Epoch 18/20\n",
      "5s - loss: 1.3228e-04\n",
      "Epoch 19/20\n",
      "5s - loss: 1.3269e-04\n",
      "Epoch 20/20\n",
      "5s - loss: 1.2760e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x129d95fd0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create and fit the LSTM network\n",
    "\n",
    "model_LSTM = Sequential()\n",
    "model_LSTM.add(LSTM(9, input_shape=(1, look_back)))\n",
    "model_LSTM.add(Dense(1))\n",
    "model_LSTM.compile(loss='mean_squared_error', optimizer='adam')\n",
    "model_LSTM.fit(trainX, trainY, epochs=20, batch_size=1, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# make predictions\n",
    "trainPredict = model_LSTM.predict(trainX)\n",
    "testPredict = model_LSTM.predict(testX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.41206869], dtype=float32)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testPredict[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "529"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(testY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 1.64 RMSE\n",
      "Test Score: 5.14 RMSE\n"
     ]
    }
   ],
   "source": [
    "# invert predictions\n",
    "trainPredict = scaler.inverse_transform(trainPredict)\n",
    "trainY_val = scaler.inverse_transform([trainY])\n",
    "testPredict = scaler.inverse_transform(testPredict)\n",
    "testY_val = scaler.inverse_transform([testY])\n",
    "\n",
    "# calculate root mean squared error\n",
    "trainScore = math.sqrt(mean_squared_error(trainY_val[0], trainPredict[:,0]))\n",
    "print('Train Score: %.2f RMSE' % (trainScore))\n",
    "testScore = math.sqrt(mean_squared_error(testY_val[0], testPredict[:,0]))\n",
    "print('Test Score: %.2f RMSE' % (testScore))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sarthakdasadia/anaconda/lib/python2.7/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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sSTKTDVVoywSyjHAHAACQcyfnKrrtvllJ7VEIZrksw7Ki56jcAblAuAMAAMi5B08uJ48N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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x126c06690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# shift train predictions for plotting\n",
    "trainPredictPlot = np.empty_like(dataset)\n",
    "trainPredictPlot[:] = np.nan\n",
    "trainPredictPlot[look_back:len(trainPredict)+look_back] = trainPredict.ravel()\n",
    "\n",
    "# shift test predictions for plotting\n",
    "testPredictPlot = np.empty_like(dataset)\n",
    "testPredictPlot[:] = np.nan\n",
    "testPredictPlot[len(trainPredict)+(look_back*2):len(dataset)] = testPredict.ravel()\n",
    "\n",
    "# plot baseline and predictions\n",
    "plt.plot(scaler.inverse_transform(dataset))\n",
    "plt.plot(trainPredictPlot)\n",
    "plt.plot(testPredictPlot)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# split into train and test sets\n",
    "train_size = int(len(dataset) * 0.7)\n",
    "test_size = len(dataset) - train_size\n",
    "train, test = dataset[0:train_size], dataset[train_size:len(dataset)]\n",
    "\n",
    "# reshape dataset\n",
    "look_back = 8\n",
    "trainX_mpm, trainY_mpm = create_dataset(train, look_back)\n",
    "testX_mpm, testY_mpm = create_dataset(test, look_back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(529, 8)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testX_mpm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "1s - loss: 3.6689e-04\n",
      "Epoch 2/30\n",
      "0s - loss: 3.0733e-04\n",
      "Epoch 3/30\n",
      "0s - loss: 2.9207e-04\n",
      "Epoch 4/30\n",
      "0s - loss: 2.5559e-04\n",
      "Epoch 5/30\n",
      "0s - loss: 2.3802e-04\n",
      "Epoch 6/30\n",
      "0s - loss: 2.3269e-04\n",
      "Epoch 7/30\n",
      "0s - loss: 2.1921e-04\n",
      "Epoch 8/30\n",
      "0s - loss: 2.0673e-04\n",
      "Epoch 9/30\n",
      "0s - loss: 2.0454e-04\n",
      "Epoch 10/30\n",
      "0s - loss: 1.8783e-04\n",
      "Epoch 11/30\n",
      "0s - loss: 1.7844e-04\n",
      "Epoch 12/30\n",
      "0s - loss: 1.7557e-04\n",
      "Epoch 13/30\n",
      "0s - loss: 1.7545e-04\n",
      "Epoch 14/30\n",
      "0s - loss: 1.6701e-04\n",
      "Epoch 15/30\n",
      "1s - loss: 1.6352e-04\n",
      "Epoch 16/30\n",
      "1s - loss: 1.5653e-04\n",
      "Epoch 17/30\n",
      "1s - loss: 1.5559e-04\n",
      "Epoch 18/30\n",
      "2s - loss: 1.4967e-04\n",
      "Epoch 19/30\n",
      "2s - loss: 1.5400e-04\n",
      "Epoch 20/30\n",
      "1s - loss: 1.4834e-04\n",
      "Epoch 21/30\n",
      "1s - loss: 1.3950e-04\n",
      "Epoch 22/30\n",
      "1s - loss: 1.3863e-04\n",
      "Epoch 23/30\n",
      "1s - loss: 1.4081e-04\n",
      "Epoch 24/30\n",
      "1s - loss: 1.3777e-04\n",
      "Epoch 25/30\n",
      "1s - loss: 1.3413e-04\n",
      "Epoch 26/30\n",
      "1s - loss: 1.3727e-04\n",
      "Epoch 27/30\n",
      "1s - loss: 1.3247e-04\n",
      "Epoch 28/30\n",
      "1s - loss: 1.2824e-04\n",
      "Epoch 29/30\n",
      "1s - loss: 1.2995e-04\n",
      "Epoch 30/30\n",
      "1s - loss: 1.2921e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x12c066b10>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create and fit Multilayer Perceptron model\n",
    "\n",
    "model_MPM = Sequential()\n",
    "model_MPM.add(Dense(9, input_dim=look_back, activation='relu')) # input layer with 9 neurons \n",
    "model_MPM.add(Dense(7, activation='relu')) # Second Layer\n",
    "model_MPM.add(Dense(1)) \n",
    "model_MPM.compile(loss='mean_squared_error', optimizer='adam')\n",
    "model_MPM.fit(trainX_mpm, trainY_mpm, epochs=30, batch_size=2, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 1.39 RMSE\n",
      "Test Score: 3.61 RMSE\n"
     ]
    }
   ],
   "source": [
    "# make predictions\n",
    "trainPredict_mpm = model_MPM.predict(trainX_mpm)\n",
    "testPredict_mpm = model_MPM.predict(testX_mpm)\n",
    "\n",
    "# invert predictions\n",
    "trainPredict_mpm = scaler.inverse_transform(trainPredict_mpm)\n",
    "trainY_mpm = scaler.inverse_transform([trainY_mpm])\n",
    "testPredict_mpm = scaler.inverse_transform(testPredict_mpm)\n",
    "testY_mpm = scaler.inverse_transform([testY_mpm])\n",
    "\n",
    "# calculate root mean squared error\n",
    "trainScore_mpm = math.sqrt(mean_squared_error(trainY_mpm[0], trainPredict_mpm[:,0]))\n",
    "print('Train Score: %.2f RMSE' % (trainScore_mpm))\n",
    "testScore_mpm = math.sqrt(mean_squared_error(testY_mpm[0], testPredict_mpm[:,0]))\n",
    "print('Test Score: %.2f RMSE' % (testScore_mpm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sarthakdasadia/anaconda/lib/python2.7/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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BZsKesyyqAuTZqNoyvz398zWSPtXnvxdLUhiGM+nXd0l6R/rfA5JuCsPwxIjGAgAAgB6u\nvVpm/7ZMKanuAcivoSt3YRg+Isn0HLtxiNd/WdIrh31fAAAAbJxt73PX82/7vp85Z5sHBWCkWA4J\nAABgF0gqd1q51UGmTdMR7oBcI9wBAADsAs7avnPunNep3DnSHZBrhDsAAIBdwForSStWy8w+pi0T\nyDfCHQAAwC7QrsqtWFAlU7nbxvEAGD3CHQAAwC7g4qRy19uWKbZCAAqDcAcAALAbrNKW6XwWVAGK\ngnAHAACwCzxxcj75oqct03jZrRBId0CeEe4AAAAK7vjZRYWPnJEkmZ62zGzWY7VMIN8IdwAAAAX3\n2PF5GaVz7npXy8zse2fjeDuHBWDECHcAAAAFN7vQUCvCrdgKIdulGUXbNiYAo0e4AwAAKLhzCw09\ndeGIJMn0zrlT5nFMuAPyjHAHAABQcPVGrG8+8e+SJLe42PXc4f0T7a9dRFsmkGeEOwAAgIKL020Q\npJ42TEnf9MLLMydSuQPyjHAHAABQcHF2c/JM0JOkSsnX7E2vlCQ55twBuUa4AwAAKLgozgQ6u7L1\nsvHM5ydfsBUCkGuEOwAAgIJzzWbnQU/lTpL8IN3IvE/wA5AfhDsAAICiW17qfN0v3Pm+nCTFK58D\nkB+EOwAAgIIzjXrnQZ9w53lGVp6cI9wBeUa4AwAAKLiuhVL6tF56npE1pm/wK5r5pabe8b57dOzM\n4vonAzlDuAMAACi6qDPnzvVry0wrd7thzt3f3/KQbrvnuP70n+8d91CAkSPcAQAAFFzX5uR95tV5\nnpEzRmYXVO7OzC5LkiLLyqAoHsIdAABA0WXaMk2feXW+MbIycrsg8CwsNeW5WHvL4x4JMHrBuAcA\nAACAreXitbdCSObcefJd8dsy55cj/fSD75F7tCR91/PHPRxgpAh3AAAABWcylTsXRyueT+bc7Y4F\nVRaWmgpkpai+/slAztCWCQAAUHAm7lTk+i2ospvm3C0tNcY9BGDLEO4AAACKLlutW221TONJrvhz\n7koxFTsUF+EOAACg6OJsW+Yq+9zJSLtgE/NqTOUOxcWcOwAAgIKy1mmxHslkwt2e5z5vxXl+uol5\n0dsyz87VNWGp3KG4CHcAAAAF9evvuVP3Pz6jF9ST1TLPe/m36LxvfsWK8zzPyMnru01Ckfzau27X\ntx77xLiHAWwZ2jIBAAAK6v7HZyRJnk0qd9Wrr5FXKq04zzdJ5a7obZmTxx7VgWh+3MMAtgzhDgAA\noOCCNLSZoH/TVjLnzpMp+CbmTmbcQwC2FOEOAACg4Fqbk5s+VTspM+eu4JW72PDRF8XGnDsAAICC\n8pzV82fu1YHmnKTVw10y56744c4v+PcH8M8XAAAABfXMuQd10+k7VFt4TNLqbZmtfe6M+m9yXhSt\nCmaL2+S+fvc+ckZ//bEvy465nTW2VsfPLo51DNgZCHcAAAAFNRktdz02weqVO9uaj1bkcKee720T\n4c46p9/8y8/rQ59+TI+fGO8iLW/5n5/Vf/2j23T8DAFvtyPcAQAAFJQ13QuIrF25S87tt8l5UUz5\n3WFuM99rtlq3sNzc8HU26viZRZ2dq+vo6QUdPXFO33nkX3Tm1lu3fRzYWZhzBwAAUFC2ZwERU1p9\ntUzX+jf/As9L21PpqWtsokoZx9lwF61x5tb4r++4TZL0A9/0dF2ydFJXLB2T/und0iu+ftvHgp2D\nyh0AAEBB2Z6Pev32uJMkz3Qqd/9w85e3fFzj4qKeELaJIBtnguHC0vZX7lr+/IP3qezG9/7YWQh3\nAAAABbWyLbN/uDPGyKVVvn/73ONbPq6x6Ql3m1k8Jk7bMi9cPqWl2blNDWtYtmeu4OWLx7b1/bFz\nEe4AAAAKqrdyJ99f9VyXBkHP2RXhoTDiJNx1Fo/Z+PcZW6dD9Rn9wBMf0IUfeOcoRjewKOoOpc8/\n96VtfX/sXIQ7AACAgjLqDi+mp5KX1arceXKb3iJgx0rDXdMkcw83VbmLnfZHScVu6vSTmx/bEBqZ\ncOfb0SyA87nwhO55+MxIroXxIdwBAAAUlNe79P8anJeGO+c2s0PAjuWck9cKd166sMxmwp1zMmP6\nQTUz4e67j3yo/XWjumfD1/z9f7hbv/VXn9fiGFb+xOgQ7gAAAArIWid/mPDRqtw5W8jK3XIjlpdW\nuZomaU/dXOXOasI22o/nPvuZzQ1wCM04GfeNp+/UxfXT7ePtdtMNMM7q1Udv1kMf/timx4fxIdwB\nAAAUUBRbeW7wlj3bqtzJbmYq2o41M19XkK6O2a7cbWq1TKdqXG8/Pvr2P9jU+IbRbMaqxsv6mrN3\ndR334o1V3ZxzOtQ4p9rCYyq/792jGCLGhHAHAABQQFFsh6rcOS+pZvkFrdydm28ocKObc2d7wt12\nasZW33J85YblrbbTYcXWtX82yDfCHQAAQAFFsRtqzp012XC3VaMan5mFuibTMLYQVJODm9wKoZJp\ny9xOzcjqYGNmxXE/jjYUzKPYji2oYrQIdwAAAAXUjKy8YdoO25W7uJCVu7mFpibjZTljtOhPSJJc\nvLnVMoOetlcXj2blyvU0IqvZYLrr2II/kayOuoExRLFrB1/kG+EOAACggBpRLD8T7s4cunzN863f\nqdwVcc5dI4qTcFedUpwuHrO5OXd2Rbizje2p5DUjq3Ol7nB3tHo4HcPwIS2KrSbj5fbjIob73YJw\nBwAAUECNpm2Hu/956ct1x/Xftub5rTl3Rd3EvBmlAWZqWq69ifnm2jJXVO7q21P9iiKrwHbmyNnr\nnqNmUEnGtbCwoetlW0zt8vIaZ2MnI9wBAAAUUCOK23PuYuMpXi+vpZW7wMWFnHPXbEaq2oZMdaod\n7ja1FYJ18nsrd9sU7ppRp2r4nou/ThOv/V7VS8k8wnhubvjrxValzPfSPHVyNAPFtiPcAQAAFFCj\n2ZlzZ42nibK/5vmtyt23HbtZS3fcvuXj225xPalMmUpF1oy2cve5fTVJ0gMPn9D80tZvAp6EsaRy\n92j1QnnlsprlNNzNDx/uoth1VQLnH3l8NAPFtiPcAQAAFFCj2Zlz95zaBXrtTU9b83znd8Lfwof+\neUvHNg6t+XBeuTyayl3sFNhYzvPUSLdW+MsP3qNff/edmx/sOpqRVcnGiownGSNjjJqVyWRcA1bu\nYmt12z3H1GjGiuLu+YMLjz+2JePG1gvGPQAAAACMXiOzWuZ3fN3TVZqurP0CL/OxsFXZKpBW5c4v\nl2RNWqXaxMoxrQVVnF9S5JUkSWXb1MMn5zc91vU0I6uKixSlofLg3oqiVribH+z9/+7mh/Shzzym\nG7/iYt3yhSf1iky4u+P2B3T5d41+3Nh6VO4AAAAKqNGM5adz7oy/dkumpPacu+QFxQt3tpmGu0ql\nU7nbxGqZ1rpkT8Ag0LlgSpL0jPmHNz/QAbTmyFUmJ/S7b7xBkxMlxRNpuJudHegat917TJJ05wPJ\n/Lps5W5Pc6GQi+rsBoQ7AACAAmpEndUyTbB+s5YreLhzjWQunF8pj3a1zKCkc1c9U3N+VVctHNF2\nrEbTbK2WWS5raiKpGtpqEu7OfviDahw7tu41ZuYbeu7Ml/TsuQcldcLdklfW3mhBy/Xt2bMPo0W4\nAwAAKKBkzl3yAd2USuue7/xOAHQFDHetOXd+pSyX7nO32dUyAxfLBYH+02u/Uo9VL9SkrWt/c/gF\nTYYVpZW77H111c6+d6ff9499X/exO57Qn77/Xjnn5HtGX3fqdt3w8M0yzmoi3cT8eOU87Y/mNXf0\n+NZ+E9gShDsAAIACamSWyx+kctfdulmscHfi7KIeO3JWUtqW6aUfge3Gq1Ptyp0f6OC+Cc2km4q/\n5PT2LKjiu1gm6IS7G66/qv21v3df39e961/u17/fdUznFhryMynglcdu0cX103Iyunf6SknS/Gdu\n25rBY0sR7gAAAAqoEaWrZfq+jDfAR74Ct2X+62efUJBuHeCPbLXM7oD1jGddIUl6+sKjWgjDzQ14\nHVEUq+RieeVOuHvWUw+1v/amp/u9rO3o6UVNqhNsn76QrI5p5PTUr7tRVkbRl+4a8aixHQh3AAAA\nBRTHrTlhgy2Onm3L1CBhMEeMUXuTblMqdyp38cYrd800YCltjbz2usvazy09srULq0Tp/EFTKvd/\nPlr7+7r3kTMqx42+z03snVbDC7ZtQ3aMVrH+5gIAAEBSZx82BevPt5NU6MrdciPutKiWSopN8r1+\n6JMP6U2//+8bumazngQsLw13JtPnaKOo72tGxTa737vliy98TTK25f7BLfA9TcTL+uI9j8trLvc9\np1oOFBtfbou/B2wN9rkDAAAooCjdh80MGO5cdp+7gs25OzffSFaXlOSVS7JpkH3syIzO7j0oa508\nb7Dv+dx8XUv1SM3lpLLVWtRk8hnPkjVGnnNbH+7Syl22LVOSvKmkHbO51D+4Tdplfd9j71Pp0Vgf\nOP+Ffc8JfKPYeJuqamJ8hg53tVptj6Q7Jf1eGIa/kzluJP2MpB+TdImkL0v69TAM39nz+ksl/aak\nl0qqSLpZ0pvCMLx/g98DAAAAekStOWGl6kDn20wILNpqmVFsNZlpy7Tp5t+t1UTjAcOdc07f84sf\n0v7psq6/LNl6wC8nrZFeuaw7X/BaPfe2v5Ztbm0wilsrf/ZU7oKJZKP6uE9LpbVOF80d1XS8JEm6\nYnHldgmHvvU1mvU9xfKkmMpdHg3Vllmr1Q5K+oCkp/Z5+lfS//5M0qsk3S7pL2q12vdkXj8t6WOS\nvkrS6yX9kKSnSfp4rVY7tOKKAAAA2JDWnLtBK3dxkJm/VbBwF8dWFZtWuyYmFKdz7vZFC3r10ZtV\nP3p0oOssN5LQNjPfUFxPAla2etZacdRucTBqtUz6le45d+VqK9ytbMusN2OV05+BJF3bs+H63htu\n1Hkv+2YFgZe0rW5iJVGMz0CVu7Qq9xpJvy1pxT//1Gq1iyX9tKT/HobhW9PDH6rVagck/UqtVnt3\nGIZWSVXvSknXhGH4UPraT0h6UNIbJf38Jr8fAAAAqFW5swPtcSdJplzccBdZpwmXhrtqVTZtQf2a\ns8mKkKfe+WeafvMvrHudhaVOOGpXzzI/t3a4W2dBk82yzTRY9tzbUnUiGVufyl29GatkO6FzIhP0\nJMmfnEqu4XuyxpOhLTOXBq3cXS7pPZI+LOnr+zz/UkllSX/Tc/wvJT1F0nPSxy+TdEcr2ElSGIZH\nJd0i6RWDDxsAAABrae3D1jsvazWmXGl/7bZqUGNim01df/YeSZJXnWzPuWtxA1baFpY75z36xBlJ\nUlDpF+62uHKXLqjSW5Utp+Gu9XxWvRG3t4PoZ/La6yR15twZKne5NGi4O6Wk2vbDks70ef46SVbS\nAz3HW4+vzZzXb+OPByQ9fcCxAAAAYB1xM5YnJ2/AtswgyHwsNMVaUP1pR77Y/jqp3HWHO62ypUDW\n6XPLOjtf1+sf/lu9+ujNWl5KqmNdlbtSUhF0W1y5c80ofb/eOXfJWFxjZVvmciNubwfRz8SVySbo\nge8l4Y7KXS4N1JYZhuG8pPk1TtkvaSkMw95/JphN/9ybOe9cn9fPSirVarVqGIZLg4wJAAAAq2u3\n7g1YuQv8bLjbihGNj3OdWqRfrfasDKp1w925+br+7z/8pCpxQz8VL2rvwmN6rHpBcr1M5c7bpsqd\n4tY+dz1tmaWSrIzUr3LX05bZy6smM6/a4c5ZOWtlCrbnYdGNaiuE9e5662/UoOf1deDApILAX+uU\nsTl8eM+4h4ABca/ygfuUD9yn/OBe5cMo71PJJB+rJqYnB7ru3j0T7a+DUlCo35mloPO9nX/poRWh\nqLzOz+jccqyLl0/q+574YPvY1526XZK0Z/90+7WT00lAKvlb+3euVVWb3jfV9T6HTi5ozvjybKTD\nh/eo0Yz16+/6rL7+hZerUi2r1NOWef+hmq45lTTVnX/BPknSUuz0pfQj+6EDVXnl9auaO1mRfo8H\nMapwNyOpWqvVgjAMs781ezPPt/7s9xPeK6kehmH/TTlSZ88ubnqgW+Hw4T06eXJu3MPAALhX+cB9\nygfuU35wr/JhFPep3ojleUalwFMj3eusaTXQdRv1TrWnGdlC/c40bKcUeerUvKqTle7nnacvfOmY\nDu+vqhQ+bRTsAAAgAElEQVSsrEXMzS3p2bO9s48Si/XOz6oe2eTPxfqW/vxs2na52Oi+T0sLdUVe\noKCevP+n7z2uT99zTJ++55h+/FXPXFG5mzWd0Nu6ztzskmy6yfuJYzPyq4NtpbETFfX/fWsF1lHV\nWe9Lr3VVz/Gr0z/vzZx3tVa6OnMOAAAANuDHf/vjetPv/3vyYJV5Wavxs22ZBevLdD3zx/bunex6\nPNuUfv5PPq23v/fuvq+PY6fpqP/MoezP1wuCvu83aq3K3Yq2zMBXZHyZdIGY2cXO3LvlRrSicufv\n26terbZMSdJWt5di5EYV7j6sZEGVb+85/p2SnpDU+pvyQUnPr9VqV7ZOqNVqF0m6MX0OAAAAG1SN\nl3XV8XvlrJVbZV7WagLf6F2XfKMkyTm7ZWMch9bKjxf+4I9Ikvbtm+p6/q7HkurOnQ+c6vv6KLbt\nzb9XXDsb7vztCXet0LViK4TAU9MEMlFy7+cWO9XYemPlnLuX3JCskKnMvLpsuBt0FVHsHCNpywzD\n8EitVnu7pF+q1WolSZ+S9B2SXinp+9I97iTpDyX9pKSP1mq1N0uKJP2yknbNt41iLAAAALtRFFu9\n5sl/0yX1U5r91DM6AWDQ1TJ9T8cqB5MHBVsG38XpR9G0Onn5pft7Tlh784e3vutz+vG4/+yh7sqd\nn77f1oWi2Fr5aUjr3QohCDxFni8vSqYyzaeVu+lqKdnEvKdyt+/iC7T3Z9+s4ODBzjX8dBNzdTZL\nR36McvmbN0j6VUk/KOkfJD1P0veGYfiu1glhGM5IerGkuyS9I/3vAUk3hWF4YoRjAQAA2FWWG7Eu\nqSeVp+j0qXb1ZvDKnSfb2rzcFmynuzSstvahe/bV53c97cuqbFeuMCkl7YySVMoE3pNPv779tTfR\nmbfmlba+chdFyf6F0sp7Ww48NY0vL47knNPcYlPXzj2sm07cruV6pIptqmEytR3fV/Xqq1U677z2\noVJgOpW7Ld7SAaM3dOUuDMNH1KcRO11I5RfS/9Z6/ZeVVPQAAAAwIvVG5oO4Me2qy8DhzjNyrY94\nBarcWevkpW2mrXDnTVS19z/coAfPNnX43tv03HOhnnsu1P93xWtXvH5mPql+BZk94pb2HWp/XT6/\nExTbc+5WCUX/dOvD+vgXntSv/cev7rtwyyCasZXf+n76tGVGJpCRk4sizSzU9drjn5Ak/b+fvE4/\nYJvyqlV98rxn6UXxYypfeNGK6/u0ZebaqFbLBAAAwBg0mrEePjqrqWr3B30T9W/dW821lx+QjJGV\nkbPFmXMXW9sOd0rDnTFGF77uh3THP39Gh++9rX3uocbK7ZjPztV15cKRrpbGqNxZQbJ0uBPufN9P\n9vVaJRz/460PS5JOzCzpkkNTfc9ZTzOyq1buSmlbpiS5ZkNnZuvt58pxU5W4oYmD5+kHfulHV72+\nZ4xc2pYZN/pXM7FzsSshAABAjv35B+/T/3j3nbr1i0e7jpvWvKzSYP+Wf2h/Vd9641XJJthxccJd\nFGcrd90/i9am4y0X1VcuqDIzX9d3HP1o1zFrfN295yo9OP0UmaBzTT/wFMuT1mnL9FZZjNQ5p7+/\n5SG9/1OPqN7sf42kcpeGu6D7+yn5yYIqknT2zLzOznbmCU7YhqpqKpjsXim0H5NuzP7kkdPrnoud\nhXAHAACQY59Nly144ImZ9jEnSc3h5txJUrnkyxpPKtBqmbF1nTbGnjDX+/im03esmC+3VF/Zmug8\nX/98wYv0T5e9tOu4b4ys8dadc2dM/3R376NndddHPqlH3/t+/eVH+++rF2Uqd/1Wy4zSqtvd9x/r\nqjZOxUsy1sobYN+6i6+5QpJUf/LIuudiZyHcAQAA5Firg7LR7ASy2Lr28v+DtmVKkjFKFlUpUFtm\nFFsZpQvEeN0ffb1gZVUzmpnpepz9ubbYtPWxN6L5fhLu1qvcxatURuuNWP/nkx/RS0/drgcfOt73\nnGaUqdz1hDtjjJQei+sNPWPuofZze6MFSZJXXb9y5190SXKNY0fXORM7DeEOAAAgx2y6jH8z6gSG\nRte8rMGXWPBMuqhKwcLdapW73rZMSWqe7m7NbPZZHMWa1ty97uO+ly5G0mfO3fzn79R1cw/pTQ/+\nbzXu+MyqY235pvvfr3+45SH92v/6nFx6j51zeueH7+vc2z7h1E/bLt3igi6od9oqbzj9+fT59St3\npcOHk2ucO7vuudhZCHcAAAA5NxHX5TKBotFcvXVvLUnlzitUuIvtGnPugj7h7lR3uGtENpmHmPGs\nWrKIyrff9LSu475n0jl3K1s5n/y9t+kVx29VycVq/s07+451fqmzgMnB+RN63ycf0f1PnFOcbk1x\nenZZDx+dW3VBFUny9yV7+C2dPqNyZtPyPekm7Huuf2Hf986q7plW0/gyC3PrnoudhdUyAQAAciyw\nkd748F/pxPHD7WP1KJZvVw8AqzEFrNxZ6+Rr8MpdvLzU9bgZWS34E+1wJEmXXLBPf/yfr5Xf0+ZZ\nCjwteX57pdJhzS/2X53y7Fxdb33nZ/XVz7xQklbdCkFSe8+65pkzmrDd4/CvuU6TT7923XFMVks6\n41c1uUi4yxsqdwAAADn1pUfPqmqT5e7PXzzZPt7VljnEnDvPKKlSueJsYm6d5LW+n95w16dyZ3vm\nwzUiq4pt6nj5QPuY8YMVwU7qLGhieip3dsBN4WcXG32P33H/Sc0uNvXhzzwu38ba15xPxtEn3Hn7\nk3H6C7Mque5xBAcPDjSOyUqgBX9CwdJCuyUU+UC4AwAAyKmP3XmkXcXJajTiNVv3VmOMkTMq1GqZ\nrs8m5i39Kne9K102m8nPsuF1Gt5Wm8fYDndRdwXu9MziQGM9dbq7UmbScQd+5yP7tx67WRc0krlw\nvW2mklTaty95zdKCSrZ7HMHUYHvrVSuBFoMJec7KLg42duwMtGUCAADk2HS08sN3s9FsV228cmXg\nayULhBStcufktdsyu+safqlfuOsOts1GJE9Ok5MTUrptXL9QJSXhLk4rd8659pYHJ0/PDfSh+/Sp\n2e7ruVhNJXPxrlo4on3RvJ662NmewPSpHlb27pEkeY3lFZW70vRg4W5yIlDdS/a6s/Vl+QOGQowf\n4Q4AACDHvufIh1ccazaaKrU2Ma8MHu7aq2UWKNw5p3blTj2hzO9TubM98w3jxsr9AvutUim1NhH3\nZSS5KGq/5szMos5fZ5xRbLU40125u3LxSb362Mf14MFv1rf3bKS+msmpqmIZVWyja0EVSSoNGNLK\ngacorVTa5fpAr8HOQFsmAABATvXfCluyUact0yuXB79ea5+7AoU765z89Pvpbcss9Zlz19uWGTUb\nK15r+rwuuZ6nON0DzzU7LZFLS+sHpPmlpqrRctex5818SZJ08V23rPv6lupESXWvrIm4obJtKnsn\nB23LNMbIlZLfm+bS0jpnYych3AEAABSMi6JO5W6ocNeq3BVnzp212bbM7lBWKa38KNzblhmllTtl\nqnWrLVJTCnw1TXKea3YWR1laZaGUrNmFhvbE3S227Ypjn03RL/2Zn+17nWol0LJf1oRtqOQiNUud\nfe3MxMS642hZiJOfzbve98WBX4PxI9wBAADk1SoVNhvFmTl3w1XuitaWaV12QZXuj77lzJy7mWqy\nymRv5S6uJ+HOZRZU6V11s6U1507qrtzVB6jczS022/MnZ/1kI3KvVXfraRWtvOimVbc0mKoGWvbK\nmo6XFDiruNIJdN4QLboNLwmwZ0/PsmJmjhDuAAAA8mqV/dRcnFTunOevOj+sH88YuaK1ZVrXCUle\nb+Wu8zgOkhDsMkFqdqGhk+kKli7blmn6N8S25txJkm10wt1yz7w1G6wM3LMLDU1HSQvkTDlZFKW1\nEqrXM3euumey7/tL0sG9E+3FUCTJVjqVu2GCfjMNszeevlOLswsDvw7jRbgDAADIqcYqi124OFbJ\nxdIQ2yBIrQVVVKxw5yTTmnPn9VbuOo9tKalqZcPd0dML8tLN4N0qK2RmBYHpVO4y2yHUlzttmSfL\n++VFja73kaRzCw1Np22Zs+W9kjptmZWeLQ38NdorjTHyp6c739dEJwiaIVZOvewpyZ545zdmdPb2\n2wd+HcaLcAcAAJBTcb3/XC7XasssDV6pkTpz7kyhwl1nzp16wl22cmfT4JNty4ytk58uTLNaK2aW\n73mK0xDoGp1701hKwln0vBt0Lpha8bwknZldblfuZitJuGu/dw9vndB+7dUXdB5kqnXDVO5e8tzL\n2l8vL7DXXV4Q7gAAAHIqbq6yUIeNFdjhw53nKW3LLM6CKskm5qtV7jqBzZVXVu6sde3WyMsvOaAv\nXPE1Kn/tN675frYV7jJz7uKotZ1C0G53tPXulTHPzNU1HS/JVCe1VEkCYNmt1na79v2ZuuqqznhM\npp10mPmXmZC7eI62zLwg3AEAAORUNkB0iaOkcjfEh3mpmPvcWScZuSS09si2Zbb2wMuGuzgT7irV\nil778z+q57/hR9Z8v9bcvTi7hUCcLm7jB2qYpOpme6qup2eXNR0tqrR/v2wayqfi7gDY4uL+oa9l\n34tvan+djYFeZfDfhz3Xv0DRJVdIkh57/NTAr8N4Ee4AAAByKrtoR5YXJ/vcrbZk/2p8r4jhziVt\npmblx14/U8lrLVqSbcu0mbbM9VohW6JysoBJPJ/ZkDxKrjE5VWlX7qKl7lbHhblFVW1Dwf4DMtW1\n96PrXdGzlzGm3f5pbedemiEquSYIdOUPvk6SND8zp2idaiF2BsIdAABAXq3SlunFkQJnu/ZmG4Tx\nkgVVTNHaMuXkvLU/9gZp62Rv5a61Gfygq47aNJjZ+XlJ0pceOaO5haQCNzk1IZdWU//3P9/d9Tov\nDYPB/v2q7Okf7qae85XJdWpPX3ccLt3i3mXD3QDzBrP8ahJUr5l9WE+eojUzDwh3AAAAOWXTQNIw\n3cGjbJPQN8w2CFLSlmmLthVCa5+7PpU7Sbr5guslSWefkgSm7Hw265yetvhE8mDQn+VkEsyiuTnN\nLzX1G3/5+fYm5F4QKJhItyZ49MuaSzc3j2Kr8nIS7vz9+zWxb0/fS1/wvd+vK3/1N1bd4y5ropKM\n9/z91XXOXJ1XTVblnI6Xde6zn9vwdbB9CHcAAAA5ZZpJK+Gy391uV4k3Gu4kFa4tM5lzp1X2pnvd\nW/6jrvz9P9LCwUskSc52r5b5FbNfTo7X19+IXJLMVLINQXN2To1mcq3WvD35vjyT/GxffObzOnbf\ng5KkheWovVJmsH+/pvZNqx+vUlHp8OGBxnH4mUlYPfDUywc6v+/7ZbZciO67a8PXwfYZ7m88AAAA\ndoTY2vY8sWWvrL3qzOGaaFfuhp1z5yVbIUhyzq26WXeeWOuSBVVWacssBb4U+DK+13pB12tbBm1p\nbO0x15yb02T68/MzrZ1zUwc71z9+VNK1WlxuajpOw92+fTpcOtD32sPcz/O/5/s1ed0ztOf6Fyo+\nd05uA4E9+3529tzQr8f2o3IHAACQQ42mbVfoFvxO6925YKq96bVXGnbOnTqrShakepe0Za5euWsx\nXrr5eM+cu9bCJPtu+tqB3q9STbZUiJtNxWmLZ6tyZ/xA/8d3faNuOe85ydhmzkhKKnfVdGVMf89e\nfcU1F+hsqU9r5hBz5vxqVfv+ww3ySiWd/13fowu++3sHfm2LMUbTP/fW5Ov52aFfj+1HuAMAAMih\nZmRVtUmr4JJf6Rw3gTyl+7oNWbnzjJFNF+LIVrDy6LZ7j+mP33ev4jip3MlbOxgZv7UASXflzsoo\nnt4nb8CVJifS7QZss6k4rfx5rXAX+Lr8on26/MYXJCfPnJUkzS81NdkOd3s0XS3pyl98y8oxjqGS\nOnHooE6X9qq8NL/t743hEe4AAAByqBHF7UCw7HWCx96os6rhsJU7P10tU9KG2vh2igefPKd3vPce\nnbvtk/q7f7l7sMpdqyrWU7nz5FZsfr6WiYlAkTzFzagd7tqVu9YcyMmkKucWk3t1br6uyTgJ6v6e\n5LnzLzxPV/7ab+jTh58z8HtvBc8zmg8mVW4sykVr76+H8SPcAQAA5FAzsqqmgaDudSp0Zdf5AD5s\npcczptOWmePK3Vvf+TldO/+IXnH8Vr38kY/IyErrBbRWW2bcXbnznF236pdVDnzFxpOiTLhTqy0z\nuY5J98xrhaWZ+YYm42U5GflTncVUSocO6/Gpiwd+763geUaNdG8+2xhsURmMD+EOAAAgh+YWm+22\nzBtf+LT+J9m1N7vuZVqbmEu5nXPXWgRlKq1qXrZ8PK3crf2x1/Na3/fKyt26wTAj8I2s8eTiuD0W\nr71aZhKS2huip1tZzMzXVY3rMpOTK6qE0RDBcisEnlGsVvAd7vcJ249wBwAAkENPnJzXRFyXM54u\nfsHzJEkTL3xR1zlmyA/jfibc5bUts96MNRUt6uqFx9vHkjl3g7VlZgNMe488f5hw5yk2vhRHimzP\ngippW6ZX7g5359LKXbBn5SIqkRnv4vaeZxS3qrmEux2PrRAAAABy6MipBT01bshMTqrylKfoqt96\nm9zklB6+7dbOSUN+GPdMdrXMfLZlNpqxXnP0Y7qofrp9bDpeVuztW/N17YqZ627LNENX7rykLTOO\nk8VcnNUNZz6fvEca7vzAVywjFyX3p95oasI2Vgl3463c+Z6RTcfAnLudj8odAABADjWasaq2Li+d\noxXs26dSKdAtz35l56R4uA/jnsm0Zdr8Vu6ywa5t3a0Qko/FLvN9J3PuXHubhEEEgUnDXSRrnZ45\n91DrJ9quDgZeUt0zaeXOW16SJ6dgz94V1xt3W2a2ckdb5s5HuAMAAMihOE4WVPGmprqOf//rX6mj\nlYOtk4a6ptfVlpnPyl29abXgT6x8Yp3qm9dqvYxj/dOtD+svPnRfMufODbAYS0YS3NLKnXVdq5ea\ndM6d75ukIpdWwrzlZAN6f8/0iuu1w/aYJNtjpMGXcLfjEe4AAAByyKsvy5PrWl1RkkpBGi6kjYW7\nVpbIaeWu0Yy15FVWHF9v5VDP82RltLTU1D/e+rA+/vknkzl3cp1tEgYQBGlVLo4VW6uSzaxeGiTX\n8b003KWV1aCeBEB/emVb5lJQ0bJX1omnfdXAYxglY4ysR1tmXhDuAAAAcsirJ9Ueb3pltedL01dI\nkqaeM9weaV1tmTmt3H3qnmNdm7q3rbOMv2eMrDGaXVhuH4tjO/Q+dyXfUyxPsslWCKXs1hStBVW8\npHXTpOHOS8fmTU6uuJ4znn7nyu/QY8/7xoHHMGrO29g/FmD7Ee4AAAByyGs0JEn+RHXFc5/b93T9\n0WWv0v6vfelw1/SMbKstM6eVu3+740jfVkazOL/m64yRrDyZzCqhthVmNrAVgoljxZFVOVu5a7Vl\nel4yly4NdyZK7qVXWRlKLzxvUjJG+6f7BNZt4toLqhDudjpWywQAAMgh16r6BCs/zr3iRVfq5MzS\nhjYxby88ksOtEGbm6/JcrMuWj698Ml28ZDWt+YZedrXMZhJmhmrLTFfLNJKsjRVkKndKr+P7Jl1Q\nZSm5fjMZmymXV1zvJ179LH3ii0f10udeOvAYRq1VuXNDLtCD7Ue4AwAAyKO0qmRKKz/OveqGqzZ0\nSc9TbtsyHz02q9t+98/0urOP9H1+vT3/Wm2ZRp1QG6VzzIYPd8n5cSNKNlBvjSEN4kE6587Ekf7u\n4w8qqqdtmeWV1bnz9k7olS+6cuD33wrOYxPzvKAtEwAAII/SD9p+MLql8pM5d4m8tWX+wR/9q77y\n+Od1uDHTPtYcYo+41jy4wMUq2aYm4mXZqFW5G6ItM7OgTRw15btOIOosqJKc4zmrD3zyYZVs+j59\nKnc7gWtv8E7lbqcj3AEAAORQ64O26dOWuVGel1SvkjfIV+WuYhsrjp0prdw3bjVGUt0razpa0hse\n/iu97vH3K2qmP+Nh9rnzTTvcuWakUjbc9W6FIOm85qxeePZuSZK3U8MdC6rkBm2ZAAAAedSq9ow4\n3HXaMvNVuctWyFqGmXHoeUbLXlkH3awkaV+0INdMAuMwbZnt1TIl2WZTgV25WmayFULy9Y889k+d\n53dquDO0ZeYFlTsAAIA8iodf7GM92a0QnM1X5a7UE+4u/dmfG+r1njGq+93hyltM9p8bqi0zM+fu\nvR//soLsuNIKmOcZ1b3SyjH0mXO3I/jsc5cXhDsAAIA8aoe7ETdi5XS1zMB2QlS0/5Amr75G0uDf\nQ6tyl+UvJdsnDLegikm2OVASOLPhrrV6qe8ZLffZi2+nVu7Egiq5QbgDAADIo7SyNsrKXXLB9ONh\nzhZUKbvMVgcbaFXdO1VWvSfctfbGC4a4Xinw2/Ppgky4m37u89rneMZoyVsZ5LzKzgx3rJaZH4Q7\nAACAHGov7T/ycJf84XK0oIpzThXbCXcbmYd4cN+ElntaJb20cucNsSJp4BvFXrrlQRrumvsO6eIf\n/8n2OeWSr2V/ZZDbsZW71u8YbZk7HguqAAAA5NEWLKiSXHD8lbvPP3BK/3bHE5Kk173sWh3Ys/Zc\ntNg6lTcZ7s7bU1kx585fXkyuN0SANsbIpa2ygY3ku1jV6Ymucw7sqeiyyy+QTna/1puoDj3ubUHl\nLjeo3AEAAOSQ2YIFVSRJ3vi3Qvi9v79Ldz98Rg89cEQf+MQD654fxValzKqUClYuVrKewPdky93h\nKohaq2UO+ZG5lLx/SVZlFyuorAynF1983opjxtuhH83T759wt/NRuQMAAMgjtzXhrr1a5jgXVLGR\nXjDzJd10+g7VT+yTXva2NU+PYte1cImXVu7KgSet3P5uVb2Vu1JaDRxmnztJ7XBZsk15zsqUVobN\n6IprhrvmGLn0+7H1+phHgvXs0H8eAAAAwFpMa0GVEbdltqtHY9wK4crFo7rp9B2SpMriuXXPj2Ir\nP1NpbIWpZ/30/6XypU9R9enX6tKf+dl1r9O7PUHZpdXAIStqrfff00y3UiitnEv31MvO02wwOdR1\nx8WWkspjvLw85pFgPVTuAAAAcmirFlRxO2ArhAk7RLlNSbjrV7mrXnGlrvil/2fg6/RuhVBOWz2H\nbZdshbmvmg2T8fRZBfP8/VUtHNyr5vHFoa49Dq1wZ5eWxjwSrIfKHQAAQA6ZLWrLbO1zN862zGp2\nW4MBRLGTn91Prk8b5CB6V7A82Eiqhv6+fUNd58xSMpaJOAmp+258Sd/zzv/O75IkTX/lc3XZf3vL\nUO+xnVwlWRAmWiTc7XRU7gAAAHJoq9oy23shjKkt0zmnUry5yp2/wXDXu8/ddJyEmYkrrhzqOnNp\nNm21dXp9FlSRpKlnPVvX/MmfDzfIMZjcOy1JaiwsjHkkWA/hDgAAIIc8u7WVu3G1ZTaaVpUhw10c\nu645d155Yx9xF72VISyamFLlssuGuk5rE/POgEZ8j7bZ3n2TsjJyCzu/hXS3oy0TAAAgZ07MLLUr\na6MPd+my92Oq3C01IlU2Oecu2OBm4M6s/Gi8eFlNXp8FUdZy8aWHuh7v2C0OBnRg74TqXkkxc+52\nvHz/pgEAAOxC/+Xtn+pUqvzRNmK5Me9zV2/Eqthh59zZrjl3QZ8FTAbhJD1ZOdh1zJscfmPxV3/b\n13Q9HnqfvB1m/3RFDa8kx2qZO16+f9MAAAB2oYl4WV85e7+kLZhz57U2rB5PuGtEtqtyF03vX/c1\ndz98prtyNz29ofd2Tnr3Jd+gP33Kt7SP+ZPDb1dQCnw9XL2ocyDnbZn7pyta8Kvyl+bGVtHFYAh3\nAAAAORLFVi8//sn2Y686MdLrt1sTbbz2iSNwdq6un/rdW/W58ET7WCOKVUrfeyaYXreCePdDp/X+\nTz3aNedu2NUtO5wiL9CZ0t72kbjUfzGUtfi+pzjT4pn3yt2BPRWdK03Js1bRzMy4h4M15Ps3DQAA\nYJdZrEe6sH66/dib2Jpwtx2Vu0984UmdW2jo9//hbkmStU73PzajkosUeUESkNYJmY8en5Ok7srd\nvvWrff201pDJBrMLLjq4ytmr8z0jm52/l/PK3fRkSbOlPZKk5qmTYx4N1kK4AwAAyJHF5UhxZjVG\nE2xs2f9Vtdoyt6Fy17se5+33ndDf3PygSjZS7JeSgNTTBnj7fSf0w//jYzp6OlmWP46Tq2Tn3JUv\nuWRzA2utGCpp38Hhq4C+Z2QzH7ONZ9Y4e+fzjFFzOvk5RGfPjnk0WAvhDgAAIEcWl6OupfaNGW1w\naLdlbkPlrnej9BMzS5JzKtumbFCSlWnv59fyp++/V9Y53fKFJyVJkbXa15zX3mhR9an9uuhXf1vB\nnr3aiH67P3hTU0Nfx/eM4ux9yXnlTpL86aRyF83PjXkkWAv73AEAAOTI4nKzq21w1Nx2Vu56wtTC\nUlMvOvMF7Y0X1Yyrsqa0Ity1HnppeGpGVj/+6N9LkkrTU9pz+LyRjO1Pn/ItembzSV191VOHfq3n\nGdlsAM/5nDtJKu1Nwt3S2XNjHgnWMvJwV6vVvkHSL0l6hqRzkj4o6efCMDyVPm8k/YykH5N0iaQv\nS/r1MAzfOeqxAAAAFM1iPVq5SfYotapM21G563m8sNTUDWe/KEkqNZZkK1Mr2jJb1T4vbXVcWIra\nz/kb3AKhZaLsa7GeXO9k5YC+ePjCDa1G2lu5MwWo3E2ki9QsE+52tJH+M0KtVnuJpA9IOiPptZJ+\nQdIrJL2/Vqu1GsJ/Jf3vzyS9StLtkv6iVqt9zyjHAgAAUEQLPXPuRi4NJdtRuWvFu1YMWlzs3rzc\nGk+mZxw2tnrFsVt03oNfkCTNL3X2xPM3ObftTd/5HD3naYf0zCuT6l8QbOznbEz3nDvlfBNzSZpM\n5x7WZ2nL3MlG/Zv2w5JmJH1rGIYfDsPwzyW9UdL1kr66VqtdLOmnJf33MAzfGobhh8IwfJ2k90r6\nlVqtlv/ffAAAgC209W2ZSaDZiv3MGs1YjWYnrLXbMtNMtrS42HW+NUamZyxT8ZKum39El3zynyRJ\n85x2R3cAACAASURBVIudjbX9TbY/XnnRXv2n1zxbpSC5TmkT17MF2gpBkir7knmMbmF+zCPBWkb9\nm1aVtByGYT1zrLVW70FJL5VUlvQ3Pa/7S0lPkfScEY8HAACgUBaXo3ZwuPwtbx39G7TaCePRV+7e\n8Lu36j//YWePPmud5Jy8NOU1Fpa6zm9XvzLhzvRM1GvMdsLGqJaWaaYtqaVg41eMC7QVgiSVK2VF\n8uQa9fVPxtiMOtz9rqRDtVrtV2q12sFarXatkhbMI5I+Iuk6SVbSAz2vaz2+dsTjAQAAKJSF5ai9\np1v5ootG/watyt0WhLt6I9bsYlPNKLl2I7L6gSferzc++G5JUrzcHRxse8+9zlg8dVcUo/mF9tfn\nvfxbRjLOG599sSTppc97yoav0bWJeQHaMkuBp8jzpWZz/ZMxNiP9TQvD8GZJvyjpv0g6JeleJRW5\nrwvDcE7SfklLYRj2/lbMpn9ubN1aAACAXWKxHiV7uvn+loQGl85bG/Um5jZTcQsfn5GUtGleWD+j\nkovloki23mmxXP6uH5dVayyZDcoz+9lFsZWWk1bOA9/wTZp+9leMZKzPe/r5+sOffrG++hkXbvga\nRZtzVwo8NU1AuNvhRrpaZq1W+11JPyHpbZLep6QV8+ckfaRWq92k9cNkn91FOg4cmNzwxNatdvjw\nnnEPAQPiXuUD9ykfuE/5wb3Kh0HuU2SdAhfLK5e35L6WysmKk9WJYKTXX05XoZRzuj08pZuuv0Ke\n3/lct7cUS41kQZULX/UqVW56gR597/slSQcPTLaX4g8yC6xMTk9oIk6qfXsvOLhtv+eDvE92zt35\nFwy/EfpOc/jMko4YX9W4mav/n+RprKMwsnCXLpbyE5LeHobhT2WOf0RJ2+VbJT0sqVqr1YIwDKPM\ny1sVu5m13uPs2cW1nh6bw4f36ORJVg7KA+5VPnCf8oH7lB/cq3wY9D7NLzYUuFgmCLbkvjZt8m/t\n87OLI73+7GJDlbihH3nsH/Wk+wqd/MaaZuc7lboj9z8h3yYfD5vytDyzKJvO/zt1YkZBPan+lVzn\nI+TxE3Oq2iQQLrmt+Xn0GvQ+Zdsyi/D3b3GhrsgLpGY9N99PUf/ft1ZgHWWN+HIl81g/kT0YhuFZ\nSV+Q9ExJ96XveVXPa69O/7x3hOMBAAAonGZkVXKxTKm0/skb0NrE3I54zl2jEas2/6im42Vd89Cn\nk2PNTlBbPnNG5TTceZWKvMx2Aq0W0X/73BNJS2oqjm27cudPTY10vJtlt3BF03Eolzw1jS8vpi1z\nJxvlb90DkiJJN2QP1mq1vZKeJelBSR9WsqDKt/e89jslPSHp7hGOBwAAoHBso6npaFH+3q1p9Wtv\nuD3ife7qkdW+qHsZfbfcqdzVz821q3KmUpHnmU5ASsfy5KkFlbJz7qxTdYeGu3jk6xaOV8n3FJlA\nXhy1N5LHzjOytswwDE/VarXflPSztVqtqWTvuoOS/rOkPZLeEobhkVqt9nZJv5Ruav4pSd8h6ZWS\nvi8Mw9FvqAIAAFAgk/Nn5Duricsu25o38LqrZaPSaMbyXeea9SNHZOud1TEb9bpKmcqdPNNuy2yN\nZXKi1FW5i2KribQt09th4a5olbtSyU9Wy5Tkmk2ZdG4mdpaRLqiiZPGUR5XMvfsxJStmflrS94dh\n2Gq5fIOkM5J+UEnw+7Kk7w3D8H+NeCwAAACFM7mULFFQvnALtkGQJLNFbZnNuGuly0d/8c0qX/eq\n9uM77j2qciuoVatSV1tm8rpKyVPJZtsyXTvc7bTKXSuYFkW5tVqmJNdoSIS7HWmk4S4MQyfp7el/\nq50TSfqF9D8AAAAMwaVL0ZtKZUuub7aoclfvqdxJ0vUPd5ZqcM2mJmzyvfnVKckznUVJ4qSi14is\nvvnEv7dfE8W2vXqmKe2ssBGbnbnC+0aVA09R+j3ZRkPF+u6Ko1j1YgAAgIIzadAxwagbsBKtBVXc\niOfcNZq2q6VSkspRpy3TNpqqxskcPG+yKs+YrjAhSfV61PX6KLbtTc3NDtsu6+U3XjPuIYxUKfDV\nbLdlNsY8GqyGcAcAAJATzjkpSsPdFq2WqdaCKiOu3DUju6JyV/c638P+uRP6ytkHkiFUJ+V5aoe7\nVrWykVmARUrCXeuaxt+asLtRl1996biHMFKBbxRl2zKxIxHuAAAAcmC5ESm2rl398rYo3Bk/nXM3\n4spdZDuVu8/su1aSVIk7lbur5h5rf+1PTsr3TNcCHpLk5rv3LIujqH1N4++syl2wP/8bl2cZY+TS\nAG0JdzsW4Q4AAGCHu+/Rs3r9b9+iD972aHtREhNsUeXOb81zG23lzlqnIK2y3bMn2fJ4KupU4hqm\nU3kzlYpMti3z/2fvzqMdu6o78X/POXeQ9OaaXeXyULZLnl22wcSAMWAgZh7CZAKk0z8SyNCLkPAj\n6ZXm10k30CzShHQS0iEhSUMg0MwQSAiBgLEdg40nbMoll10ul+2aq96s4Q7n/P64V9LV9J5ePelJ\nevp+1mJZurqSzqs3cLf2PnvHwR0WF2teM/CCajaw34K7Lo2q6CUd/8wxc9e/GNwRERERdcA9ueP4\nh399pCszwO546AgA4Jt3PlEtQ+xWWWa5FLLD3TJDbSDjwLSgomYwCtUAsjzjDoiyREpWywD9QhF3\nPXwM8ydP176m50MaDSMVRJ91p6zsieyzoHM1TBzcae6561v9VZxMRERENKA+/tWHAAAvv/5cTIx2\ntpNlsRTiuumfwVai2h2ySw1VymWZRnc2cxeG1cydzDSOLSiHZuPPfV50X4jK17jvwAl88rYSrpmd\nrn1NP4CChunTAOqCj/1ZNRO6DhgVBXf5+QJGe7wWam79/LQRERER9YGHDh1D0S8uf+IKHDgyhxee\nugc3HP9JtSyzS5k7U2mo0vnMnTIhjFR43jPPg0bzTNvo1ddU78Rf48JcHs87dS9ecvIuAMBpeyx6\nTT/K3FWawPQZNTYG1SSQHVTTpSg4/8dbH+nxSqgVBndEREREHWPwD0f/HP/ljo907BVPzRYxP1vd\na6a6HNwJGQVdJgyWOXNlQq2jMkzLwmjGgSebZx5lcn5fXAZYzBfx7OmHKoePOxsAALpYjMpU+zRz\nt96U90Auzhd6vBJqhcEdERERUafIeE+ZXujYSy4UfIwH1eCu2w1Vyq9rgm5k7qJALO1a0C0uQ4Xj\nJG5HaynlazOhp5zxaI2L8/FrcqfRWvDjPZDJ/ZHUXxjcEREREa1SEGpABnAuvL/zr601pvzqCIBu\nZ+7KJY4m8Dv6sro8xkFZsJREKJpfhspEcCft6LZXLNWcU7IzAICNP/gaMmGRmbs1Us7cWR0ek0Gd\nw+COiIiIaJUWCz6ssx6HmjxZOeaFneko6PkauxeqM+AqTUm61VBFCgRCVoald0p5z52wLFhKQCeC\nu2JimLlIlGWWA9jKKISYGYnaedhzp+GYoGvNZahWEJfSWszc9S0Gd0RERESrtFAMIOza7NKcN9/i\n7JXx/BCTQbXM09FRoNOtzJ2M58uZTgd35W6ZlgVVl7krz70DajN3yo1ul0tRy8ToWO19Zu7WhB9n\n7raVTi9zJvUKgzsiIiKiVVos+ICsHR2QDzrTdMILNGxdDbRSOsoIdq2hihAIhQI63lAlkbmToia4\nezq1uXJbpjOV2yr+GuvLAK3JqZr7juuAuq88d/CC/NOcddenGNwRERERrdJCwYewaksHgw7tS/L8\nsKaBxXiwCGRGupatEgIIhex85k5HQapwXCglKw1VjJA46m6snFebuYtKNK+af7TmteyNG2rue4cO\ndnSt1JzZVA3Cg5MnlziTeoXBHREREdEqRZm72mAo1J0JjuozdyNhEXJisiOv3YyUceauw8GdCQIo\nmCi4K78HAGPbOO1M4Otbb8Dht7yn5jlWi4zc6Fim6XHqrt976zNx64Y9AACfwV1fYnBHREREtELG\nGGhjKvcXij6Eqs3UdTJzV95nV6a6GNwJgaihSgfLMnWphNSpI9HrO07cLTMeYm5FAdzDY+fD3ryl\n5nnKcdFM2mUDlV7YOJGCuyXK3vknjvd4NdQMgzsiIiKiFfqjz92Hd/+v2yr3FwsBIEOkVQbiyCUA\ngKBDHQXryzKB7gZ3UnQ+c/fkhz+Iq279TPT6jhN3y4zLShOdLlNObdAm3eb7Ci0p8L/Of2PH1kft\nM24aABAUi8ucSb3A4I6IiIhohfYdmsFiMcD/ueOHOJmfRtGLgjtXOpCIgpbO7rmrfS2VSXfktZsR\nQiBEZ0chlJ6sjnKQblyWiThzl9g7GIS1TWksq/m+QttWKKhU9TUzIx1bKy2tvNdTd3jIPXUGc9pE\nREREZ0A4BdxduhX3/ujbuMp7K4QTwFEOlFAIAQQd2nNXWmzsuqnc5uWKnSAFosydDmG0hpBnngv4\n0d6jOHTrndiTfH3XhaVkNXOnFC46ewL7n5rF1Fjt12Wp6ns/OLYLV8wfAABcf9lW3PvICQTXvQtn\nBbMYu+66M14jrYyQ5eCOs+76EYM7IiIiojMRd8cMEaDkh0AqhGu5UHHQ4oX+Us9u24kTsw3HVCrV\n5MzOECIeYg7AhAGEPPMxA3/3T/vwO/u+XnOsnLnT8Z47IRV+6w1X4eDReezeWVtuailRue2L2vLN\n33nTHlAPxAG3Dpm560csyyQiIiI6E6YaeBR9H0IapC0X+UJUWvj1B+7qyNscPzbTcEx1ca6bKGfu\nABh/ddkZP9ANx6TrQCkJaaLHhJJIuxYuOXeq4dxk5m7PJWetai3UGeWyTNPke0u9x+COiIiI6Ewk\nhpYXghIAwLUceHHCbjF1CMVgdU0nglDDXpxrfOsulmXWjClYYemd54c1XUSFaQwALNeFpQRU/Ji0\nWheSJYO7zds2tDyP1k75+6U7POSeOoNlmUREREQrZuCc91DlXimMgruUcmsyeoWgiJR15iWURS/E\nZDDfcDw56LvTHFthvlyWGbRfWqqNwbs+eisu2D6O33/7M/DwE9MNIxyAqKRUSVl5bKnmMMmyzLHr\nfg7ekcOYuPEFba+JukCWS3ZZltmPmLkjIiIiWiExMgc5Ug26SmYRADDpTgC6enmVDxqboaxEsRRg\nwl8AAPxgw9XV928x/60TXFudUVlmuQTzscNRpvGPPncf3CbBXXkUQkp7AAB7pHWny2TmTo2M4Kxf\n/TVkshe3vSbqPMlumX2NwR0RERHRCslMbank4qZ7AQAbUpOASQR3/iqDOy/ESBiVdu4f3Vl9/y6W\nZTqWRFjJ3LUf3Hl+48W+06RjqEy5UIngzhptHdwpWc3ciS5mK2kFynvumLnrSyzLJCIiIloBbQwg\nay9sTSrK4m1ITQHmSOX4qjN3XohUGAVBRVkN6FYznmA5TjJzt4KyzGbNUxzTLHPnQgpR+brUEjPq\nhBD4h+0vwSZvBrsZ3PUFyeCurzG4IyIiIlqBMDQQsvmF7YQ7UXN/1cGdH8DV5eCuGtxoz1vV6y7F\ntVV1FMIKMnclP8Suxadx8/E7MfNDiakxF9mTTzScJxwHQgg8md6C8wpHkTr3vJavKQRwKLMNhzLb\nVvx1UHcIBnd9jWWZRERERCsQal3TKTMpbaUAkeii6edX9V7FUoiU9qCVhVAq3Du+GwDgnnPuql53\nKY4tE3vuVpa5u3ThcYyHeZz8xtexc8sonjWzt+G8cknp17bdiK9uuxGjz3hmZxZOa4KZu/7GzB0R\nERHRCgRhY1lmWcpyYfxqd8zSKgeZF70ouDOpNH7jtZfj418x+N6mZ+KvpxpnwnWKY6kzGoXgJcoy\n9cw0MjPHkJcuMrpUc15579z/8/prMZq2IYRAK0s9Rj1ixQ1VNOfc9SNm7oiIiIhWIAx1y7LMtErh\nsu074T0RdXT0wtWVTxZKAdzQA9w0sudMAUIglGpVr7mcKHN3Zg1V7EQDlY0nD0GLxkvNcubu2uyW\n6GtaAkO7/lPO3GGFMxBpbTC4IyIiIlqBpTJ3Sir85muvwLiO9ogV/NUFdzMLRaS0BzUygpTT3aCu\nLNpzt/KGKl6gYZnEv4vWlf2CSex6OdjKQ8wNM3d9iWWZRERENLS+8sMDKJYCvOXFu9t+TlC3585/\nehfsHQcq9x1bYeeWcewHUAxWF9zNTy9AwsAaGYGlJP7zW6/B1Gj3xiAA5W6ZceZuBXPuPD+EZarn\ny8CDbUIcSJ+FfaPn4WUn7oyOr2BG34bxqMR143h3v2ZqH/fc9TcGd0RERDS0vvnvj0O4edzyoova\n3t8VJLplPgu34PxnpfH5pw7UnJO2ouxUaZXBXX4uGrHgjo8CAC46e3JVr9cO25JnPArB1tULfqsU\nNZMpKQcm8W8rbLvt19y1fRy/+borcMH28bafQ90lrbjwj8FdX2JZJhEREQ0t66wDSF11G+46em/b\nzwlDXSnL/MXnX4lnXbCr4RzXjoO7Ve65K84tROscaT0LrtNsJc9oFEJ95s72ojEQRenU7L1b6Yy+\na3ZvxkSXs5XUvkrmjmWZfYmZOyIiIhpK2hioTYcBAA+dehjPOuvatp4XagOhQghIKKmgoPCGi16N\nDalqVi1jO0AIeKvslhkuLgIAVCazqtdZCSkFjIr3Va2kLDPQSCf23NlB1CXTkzY0W6OsG0pKhBDM\n3PUpBndEREQ0lIqlM7s4XSz4gAyhEpdRz9/5nJpz0ra76uDOGANTiEob5RoGdwCAcnC3koYqfggr\nWZYZf+2BUE27ZtJgklJACwnJzF1f4m8aERERDaV8qRq4GNP+846czkNYPtKqdcCVsm0YA/hh8j0M\nSn77AWXRC+HEe/bWMnMHACh3RFzhnDs7WZYZl6QGQjFzt44oKaAh2VClTzG4IyIioqFUSGTu7jvx\nUzw2c7Ct5x05tQhYHkad1vvgUq4FaAU/Mfft7/5pH37to7didqHU8nlJiwUfmbAIAFCjo209p1PE\nmQR3fjQKoTxGwdFRYPvi63cxc7eORJk7AWgGd/2Iv2lEREQ0lAql2sDlj+/9i7aed/j0LIQ0mEy1\nDrhcWwFaIjDVzN3tDx4BADxxbL6t91ko+pgI4oYqGze19ZxOEVbU0dL4K+iW6QewTQg/7hRaDu4m\nJ0dhmLlbN8plmWBZZl9icEdERERDqeidWebh6NwsAGDMbZ25c20FoxUC05j5aveaeLEQYMKPgjt7\n09oGd9KOMnd6BZk7vxiVYYYqCu5ScXAnHBsSDATWi6gsk5m7fsXgjoiIiIZSEC4fcGhtcN/+E5Vz\nC6UA817UwXLEbr0PzrYkoBXCJsFdqNvb4LdQ8DEeLEI7KajM2o1CAKqz6FbSUEV7UblpGI+BKJO2\nA2UY3K0XSkpm7voYgzsiIiIaSu0Ed3f+7Cj+7MsP4pPf3AsgCu6EFWWoRqzWAZelJKAlQjQGd6bN\n7i2LRR8jYRFmZG332wGAPIM9d6EX/btou3YmnXAcKMMsz3qhWJbZ1xjcERER0VDyg+UvThcKUebq\nroePA4g6QgorOjbqLJ25My0yd7pFcHfg6Vkcn85X3zvvIRWWINY4awcAMs7c6RXsuQtLUebO1Ad3\nts3M3Toi47JMwbLMvsTgjoiIiIZSs8xdWHfBaikJMTIDZ/dPkPfz8PwQiIO7Ebt10GXHmTsI0/Ca\nukVZ5rv/+Af4vU/8qHI/P78IBbPmnTIBQDpxcOe1H9yZ+Fzt1AZ30nEwb0WBsNlyVodWSL0ipUDI\nzF3fYnBHREREQ8cYg2/++xMNx31dG8x4QYjUZT+CmjyJB08+DD/QibLMZfbcmWgkgFf3mu3uufNn\no66adi+Cu0rmrv2yTF0py3TqHhE4mD4LX912I8Tbf6NTS6QeUVJE3U+ZuetLDO6IiIho6Bw6toBT\nc8WG40HdBavnV7MTxtSWZS7VUKW85w5oDBiblWU2OxYsRMGdMzHe8n26RTkrL8uE33zPnfZKeM+b\n9mDDs67Dhbt3dGyN1BvlUQiCmbu+ZPV6AURERERrrbzfTqjazFR9IFbyq8HeXGkB6TBZlrn8njsA\n8MO64K5J5i5osv8vXIi6crpja5+5sywFDbGi4K7cWbO+LDO9ezeusB1csWtjR9dIvaFEVJYpuI+y\nLzFzR0RERENHqXiotqoNXuozd6VEWeJscTEqy4yfk7bSLV+/eebOQG09iLlgpuH85P4/P9DIFwOU\nFqPmKjLdOojsFktJBEKtqFumiANBkwju7Oe+ELKhTJMGmYzLMoXWbXd+pbXDzB0RERENHWMACA2h\narMP9Zm7ou9VrpbmS3l4VgioAAICrmodtJTn3AHVPXdy/BScc/fhu3OP4ZX4YM35QVi9SP6fn78P\njz45jd99/LvR81KpM/oaV8OSEqGQK5pzJ+IMpbGqwV15GDqtH6rcUAWIfpGE6O2CqAYzd0RERDR0\nglA3ZO0AIKgbXVAKvcrtRT8f7blTAWzhQCxxUWspAVPO3IXxa4oogAvg42enctCJsrZk5m7/U7PY\nWTheud+L4E4pgXClmbv4XBM3YwEAZTG4W2+UikYhAIAJ2//5oLXB4I6IiIiGThBWG6PUHNe1F6tF\nv1S5vejnowYrKoAj3fqn1hBCQJoosKlkA8NqoPMXD/wN7j32QOW+XzeWwdXVoLInmTsVZe7QZnBn\njIEs7y1MBHTM3K0/UsRDzAGgyTgR6i0Gd0RERDR0osxdY+BSH9yVEs1QFoIF+EEIYflw5fIBlxJx\nWWYi+5d0JF/NztU3VBkJC5XbvQnuxIr23AWhqQ4qTwR3KpHFo/VByWpwZzgOoe8wuCMiIqKhE4QG\nwolGIdy0/SbI6XMAAH5dcFfwE2WZ4RxOzRchVIiUtXTmDgCkifbkFcM4+ydqm09YcfBXXk/SaNDb\n4E6VM3dtlt0Foa4Ed4LB3bomZbIsk8Fdv2FwR0REREMnCDXU+GkAwIUTu6C8aJZcfeYu71WDLM8U\ncfuxWwEAk+nlxxMoRMFdvhKo1QZwSlaDu/qyzH2j51Vu9ypzFwrVdlmmHySCO5UI7hyWZa43ycwd\nyzL7D4M7IiIiGjp+oCFS0Ry5neM7IBEFWqVEd0hjDPJ+bUmlveMxAEDGbj0GocwRUXav4MfD0usz\nd7Ia+IR1F8kn3cnKbdGDUQKWlAhWmLmTJs7iJIK7ZKBH64NkWWZfY3BHREREQyfUBsLyII2FqZEM\n5heji9TP7/1m5ZySHyJE8/1y6TbKMstz8PJBNK+usSyzGvjUZ+4A4NBLfwmbfuENsCYmln2vTitn\n7oQxbZXe+aGGFWfuZKIsU1iq1VNoQCkpWZbZxxjcERER0dDxAw1YfnUQuYkuiUpivnLOXN6H2nAU\nABDOT9Y8P2UtXyqZjs+5/0D0GiOp2kAnOUohCBqHQZ+e2okNL335su/TDdGeu2i97TRV8QMNicY9\nd0IyuFtvJMsy+xqDOyIiIho65VEIKRkFd8IuNpyzWPAhnCKEUdBzG2oeS7cR3Bk/aiZyYn4OAJBJ\n1wY6oalmPYImF8nTC6WGY2slytzFpXf+8oPMo4Yq0deTzNaF8/OtnkIDSrGhSl9jcEdERERDpxR4\nUddLFQV3ulhtkGJMlEUrlgJAhlDCAnRtYNZOcOeVJIwRla6c2tRm53S8X+n0XBEPPHYSAPC8q7bj\nuku2IO1aePn1557hV7d6lpIIVpC5CwJT0y0zH88BDAv57i2SeqJmzh333PUd7nIlIiKioVOI58hl\nVAYAoGe2wPg2hO3j4LEZnL9tCgUvBGQY7Y0zdcGdWj64K3kGZnEcIjMHL/RgUJudC+JM14c//yPM\nb7sD9vmjuOjsS/CcK87qxJe4KkrKauYuWD5z5wdhTXD3mbNvxo2n7sPLXvySrq6T1p5SiYYqLMvs\nO8zcERER0dApxbPn3ERjFD0flV5+8Ev/BgAoegGE1LCEDYS1l0zt7Lm7YtdG6MUJCGlwLH+yEtzZ\nfrR/z487UZ72T0COzsLa/DQs1R+XZpVRCGhzz11iiLmWFk47E/jqWc+Hyox0dZ209mrLMtvrpkpr\np6OZu2w2eyWADwG4AdEwlx8D+L1cLndf/LgA8F4A7wKwA8CjAD6Sy+U+3cl1EBERES2lFJYAAaTi\nDNwtL7oIXzrwUwCAe+mPcTx/IwqlKHNnSxumrixzxM4s+x5veMEF+P5nozEGi/4idDznLq03wMcM\nSkEQlYBa1cyYkqLpa601SyUyd347DVXCyp67EpM565qU1f2Y0Pxm95uOfTyUzWYvA3AHgAkAvwjg\nlwFsAvC9bDa7Mz7tQ/H//hbAawDcDeBT2Wz2rZ1aBxEREdFyFkvRPrgRJwrunnfldiCsfuY9W5pF\n0Qvi4M5qCO7GnfFl38NSElPpMQDAwROnMLcYZQvteL6dF/hYKPgQqho8SdUfe5gsJRJ77pYvy/QS\nQ8yL/fElUJdIIWDYUKVvdTJz9z8BPAXgRblcrgQA2Wz2LkQB3Iuy2ey/APhtAB/I5XIfjJ/z7Ww2\nOwXgQ9ls9h9yuRzDfyIiIuqakhfi8/+2H4dOzQA7gDE3aqhi27ImgLvjp0fxwzuLSF9n4CinoaHK\nuDvW1vvZIgoe//kn+ytz7iwRddH0wgCeH3XtrJD9cSm00lEInl/dc1eIxzpYqj+ykNR5Rpb33C0d\n3P10/wlsm3AwpXzYGzetxdKGXkeCuzhAewmA3y4HdgCQy+WeBrA9PuftABwAX6x7+ucBvBrAHgD3\ndmI9RERERM3cev/TuPX+w1AbC3AAjKai4E4KASSaWd7+s6cBGe2Nc5UN6Npip3L2bTmpOLgrhAVA\nRCWajoqDuyCAF4RAInPXL8GdYyUbqrQR3AUaChpGShT96GtIOezbt26V5xcuUZZ5YqaAAx//OFIL\nBzEDwH7F63H+a16xNusbYp0qy7wyfq0nstns/85ms6ey2ayXzWa/l81mr4jPuRSABrC/7rnl+5d0\naC1ERERETRX9ECK1ADl2GgCQUm7zE2W03w4AHMuBCe0zej837sYpbA8yvQAAsOPgzg9D+IGGBJxX\nCAAAIABJREFUUInMneiPMrexjFMpy9RtzLmrZO6UhefG3T5vedFFXV0j9U4lc7dE4H/y+AwuXThY\nue9/80vdXhahc2WZW+P/fhxRE5W3AJgE8N8A/DCbzV4T3y/kcrn6vxBz8X+XL14nIiIiWgUpBFJX\n3l65n0p0ywyOng+18SikW4CQGjITDeBOWw5MfgzewUtxw8W78PKrL2/7/dx43hssD9bGowAAR9qA\nBnwdwA80YFUvkE3fBHd2td19O90yg3iIubJw/lnj+OTvviDKhtK6pOXSJbvGGCx89f9iYi0XRQA6\nF9w58X8PAviFXC5nACCbzd4NYB+A38HyWUKzzOOYmsrAstRyp/XE5s3t1d5T7/F7NRj4fRoM/D4N\nDn6vImNjKaBYvb9lw2T13yZwEDx9IZxdD0KkFuGc/SgAYHJsBIBAePwcXPn8y3HxOe0PF58cjYaj\nC7uyawWjmTQwBwgF/Nv9hyFkNaAbm3D65ntlOVGGcSxtLbsmZVtQRkPay5+7HgzD17gUY0U/G6Mp\n1fTf4gf3PAlx4JGG4734dxu271Wngrty9u0b5cAOAHK53IFsNvswgGsB3AYgnc1mrVwulwzzyxm7\nmeXeZHo636HldtbmzWM4cWK+18ugNvB7NRj4fRoM/D4NDn6vIlobfOpbe5G+rnqsuBDihIz+bf70\n3Tfgv3756ygCUJsOV08Kqp9PeyV/Rf+WNqIPpdX4dOWY8aOMVrFUwn0PHoGzuxrczS0s9s33ynKj\nz+5nT88Dy6xpdq6AbSYAVKpv1t8t/H0CdFyyO3t6HqLJv8Vt9z2Fi+xRjIe11+7HnjwBmVp+RmSn\nrNfv1VIBa6f23OXi/zYrXLcB5BFl8CSAXXWPlwuy93ZoLUREREQNnjy+0HAsueduNG1jNBXdl6nq\nRWlyYLltrezSacvEKIypLU90EnvuojerNqXQ6I+yTKAa3LXbUMXVAcQaXrhT72gV5YdMi/2Yk6Mu\n5q3GWZDB7LK5HFqljgR3uVwuh6gxypuz2Wxlx3E2m70EwG4A3wfwL4gaqryx7ulvRjRC4aFOrIWI\niIioGW0ad4C4Vu3n0rZsbJySsdKV2469su0hW6cyDZ020yoNowUWgiijkCzLNGL5QGqtmEpHxOUD\nTs8L4GgfMp1e9lwafOWyzFbBnWMrOLrxZzmYne3quqizc+7eA+DrAP4pm81+DFEDlQ8AeBrAn+dy\nuZlsNvuXAP4gDgDvBPAmRGMQ3s4Zd0RERNRNxgBy4kTNMbeuW6YtGoO7tJXCMy6exE/2HcfZm0dX\n9J47No9CHKwNjlzLhimMYS5zChC6LnPXP8GdEO3NMgMAXfIgYaAY3A0FYZczd17Tx4tegHHdGPjp\nfH9usVpPOhbc5XK5b2Wz2RcD+AMAX0K0Xfk7AN6by+XKOdh3AzgN4D8CeB+ARwG8LZfLfaZT6yAi\nIiJqxg9CuNl7ao65yqm532x+XcpK4V2vvgzFm7PIpFY2EmFqrHHHiqUUdGEUcmQOwi5WRi4AgGs5\nDef3ipFROakOl//8PSwWAABWprEUj9ahOHPXakxG0QthmwC+ULBN9ec7XGwsjabO6uh0yVwu931E\nJZitHg8AvD/+HxEREdGaKfmNQYoUtSWTjmoMriyhIIVYcWDXiq1UtVRTGEBo6FIK/oErceWNfTT2\nV8Xt7tvI3IX5cnDHzN0wkPbSZZlFL4SjfXjShp34+fHnGdx1W6caqhARERH1Nc9fPkhptueu02yl\nAMRNVqwA0i0CoQU9vwGW6ujn7qtSKctsY89dWIiCO5licDcMRBzchV7rskxbB5BubeaawV33Mbgj\nIiKioVBqI7jLJC5Gy500d47tWNX7/uaed9TcV0oAcQdN54IHAAAys4CJ0f4pyQSqDVV0sHxZpinF\nwR333A0FuVxwVwqQ1iVMbJyAunwPdPxBQbDA4K7bGNwRERHRUPDaCFImEsHJr17xS/iTGz+ICXd8\niWcs75INu/HSs19WuS+kqQR3yZELn/6vN6/qfTpNyPYbqqAYTYZfyxlm1DvSiT6I0F7zskx56jhs\nEyJ19k5c8Fu/hZ/c/C4AQLC4uGZrHFYM7oiIiGgolLzaIOXKDVc2nJO2q9kzR9mwVWfKNH9u67Mq\nt4U0MKbxEkxK0XCsp1R7wV2oNWTcNZGZu+GgnDhzV2rM3GljYJ86CgBwzz0XQHVmYthijx51DoM7\nIiIiGgpeUA1SJCTeueetDeeknOqeN6uD+++UrF5yCVHN3PW1uCxzuT13RS+Eq+PgjnvuhoKKgzXd\nZBTCfN5Hyo8b7ExOAgDsyvkM7rqNwR0RERGtO0dOLSJfrJ0Zl9xzd/WmPU2ft2myGpw0G4twpqQA\nSvv3IJzbgAsmzm8I7n7hold27L06Jg5Il9tzVyhFA8wBZu6GhbJd+EJBN9lDNz1fhFv+eciMxOeX\nu2v2zxzH9YrBHREREa0rhVKA3//rH+P9f/PjmuPFUgijJSQU3n7565s+95Jzpyq3Oxnc2ZaEnt4G\nb991SDsOkCjL3JHeiRfuvKFj79UplT13y2Tu/EBXL+a5524o2LbCvJWBmZ1peMzzNVJhCQCg4rmH\nVnnoecDgrtv6p98uERERUQeUM3bT86Wa4wXPhxjROGf0PFhtBG6dLMvMpGz84ot3Y+eWUdhK1mTu\nms3W6wftNlQJQ8PM3ZCxLIF5awQbFo9C+36leyYAhNogVS7TjTN3lqUQCAkRsCyz2xjcERER0boS\nhM3LCPMlDxgBXKu9oK2TmTsAuOnaswFEDUiSwZ3boaYtnWYqmbulyzJDbRKZOwZ3w+D4dAHGirJy\n4cwM5ObNlcdCrSvBXSVzpwQCoWAzc9d1LMskIiKidSW5t26xWM0UFPwok+da7WXK2snunQklJUSi\nLLN/M3dRQxUsk7kLQpZlDpuzNo4gr6LvdbgwX/NYGJpqg504k2tZEqFQQMjgrtsY3BEREdG6UvJD\nwC5BbT6EP/zXv8NMaRYAUAiiC053mWDq6i1XwpIWrHJw0wUy0T2z3WBzzal4iHmLTGhZqA0sE120\nl+ef0fr2sp87B0UZjzfI52seC7WBowNoZUPEP0O2kgiF5J67NcCyTCIiIhoYJT/Eg4+dwtW7N9WM\nF6g/x96xH9aWp7AI4N7jP8ULd96AYpvB3Tsufyu00ZCie5+BK6Fg4ttpy+3a+6xGec8dlmmoEoYa\nysQBoOpeQEz9w7YUnLFR4DQQLtZ2zAy1gTS6MicRAJQSCIWCYOau65i5IyIiooFx6/2H8Yk7/gnv\n/bcPohAUmp5T8jRkploqZkwURpWCuCxTLR9MdTOwq3/9VJ9m7kRliPnSmbtAGyijoZWCEAMwv486\nwqSi/XR6sS5zF2pIGCDxM24riUDIZUt8afUY3BEREdHAmM97cM7fC0/O4+Dsk03P8fwQJtGw5PFj\n0zDGoBSWg7veB1OWqGa4+r0sc7lRCGFooEwIdGmPIvUnETdL8efr9tzFmTuTyOJaSvZd5u6jn/g+\nvvNHf7VsN9hBw99CIiIiGhjJxJDdostkyQ8hrGojlbtyR/DGy3wE8KEAuH1QBqkS+/n6vaFKq8zd\nj/YexUMHTmPPhZug6i7maf2T6Si48+YbyzIto4FE2bSlJELIngZ32hg89tDj2J4KoCHxyrs/BQCY\nvX03Jm98fs/W1WkM7oiIiGhgFDy/Unfk6+Yzs548sQChqo8JGeLkTBFCRheW7ZRldlsyuLM7OE+v\nkyrdMltk7v7qG3sBAGdtzGCTCWEULyuHiYo7YfqFYs3xSlmmTGbu4j13WsNoXd3PuYYeOnAKpY9/\nFCZYrDmuC/kWzxhMLMskIiKigVH0vcptP2we3P3gvqcAy4cJomBDTp7AydkCrG1PAOiPskyVKMtM\n90Gw2VS85+70zNIXv/liEDVUYXA3VCwn+n7rug6YQbmhSjJzZ8V77gCYHmXvjp5cxERdYAcAsNbX\nzy2DOyIiIhoYBS8R3OnmF4mWbSCkgYOobEym8nhw5l7I0WgkQj9k7iyV3HPX+/U0U26oMjPXvHFN\n2XzBh4Jmp8whI60o46z92t/DMDSQMJUxCABgyXjOHQDj9ya4K83NNT3u5YtNjw8qBndEREQ0MIpB\nNVvXrCwzCDUCFX06bwfjleOPFh9c8nlrzUp2y+yDYLMZGZfVicrQhubmFr2ooco6y4DQ0pRd3pNZ\nF9zpeDRGsizTkvDi8mNdXPrDgm4pTc82Pe7NLeDBA6fw2OHmjw8aBndEREQ0MEpBMnPXGKQVvRDC\njcoI3WCqclwneoLsHN3evQW2KTkgPWWleriS1oQU0BCQpnVwd8HiUxh7Yh8sE0KwLHOolDN3Jqjd\nkxlqA2F0JfMLALYSKMbl0HqxSWnkGvBnWwR3Cwv42BcewAc/fc8ar6g7GNwRERHRwCjVZO4ay7uK\nXgCZioI7J5ioHA/i6G57+mxsHdnS5VUur6Yss08zd0KUg7vm3TJt7eMNR/4NN+z75yhTw8zdUFF2\nvOeuPnMXmqhMV9aOQijI6Oc87FFwFyzUjmw4fP0rAACluflmpw8sBndEREQ0MLzEhWQyi1dWLIWA\nHc2zUzpTOe6b6NiYPdblFbZHJhqWp/p0z50xBlrIqPNhEzuKJyq3BcDgbsiUgzvT0FBFQ5raPXdp\n10JRlYO72tEJayWo31s3uTE6vtCb9XQLgzsiIiIaGF5YDeiaBXcFL4Cwo+PKpBCcPAsAoEV0zOmT\n0sERUc0q9uueO60NtGiduUvHQ+HLWJY5XFQ5mK8bAh4GuqGhSspRKFm9zdyFxdrgTrkOSsKCzufh\nhh52LzzRk3V1Gn8LiYiIaGDUZu5a7Lmz4uBOp+A/fhXk2DSkG13YuVZ/zJQbcdKV2/065y7UBqFQ\nyITFprPJGoI7Zu6GirItaIiGPXc6DvaSe+6EEEA89LwXe+60NtBe9PP6yMhOjAV5iLPORlE5sIsF\n3HL4O9hWOo25B5+B8SuuWPP1dRJ/C4mIiGhg+GFQuXgpNZlzV/RCCNuDggVVPlNXMwhOnwR3KUeh\n9LProFwf4oWi18tpShuDxzI7cOX8YygeeAzpCy+qeSylazOnDO6Gi6UEtBAwYYA/+Nu7cN6khZeb\nx6DEOQAaM7kiMwKgN5m7hYIPW0dB508mLsHxyR14e9pFSbpI+4vYFlcElGabj0sYJPwtJCIiooEQ\nhBohqsFdIWicT+UHIWB5cGUaUsZBk042L+mP4M61FfTCBug+3u5jNDAT71HUpdosnR/oxsyd3R//\ntrQ2LCURQiEoeTh0fAHZn/0Y07M5bEhvA1CbuQMAayQO7tZoj1swM4M73vEfsOHlr8RfLZ6Hc0yU\n9X/7q67Axot348DhOcwrB1u86cpzSutg5h333BEREdFA8PwQQlb3f82UZhrO8QMNoQI40sUtN12E\nrRsyMDrZkr0/AhDX6f+B36ExCON5fKZuX5UfaKR0bXBnbdy8Zmuj3lNx5s4r+VAmrDTYmfSj7pOi\nbqi9NToKAPDmOxfcaa9x3y0QNQM6dPcDAIDT3/pHPPrULOy4u+6O7RswMerCsRWKsna/q7cOOmcy\nuCMiIqKBUPI1IBLBndc8uIPUsISFrRsy+MA7rqvJ3FmyP4qWrr9sG6bGXLzzVZf1eiktRQ1V4kvF\nunb3UXBXe2E9edNNa7U06gOWlAiFAkKNVx/9IbaVTsePRN1VVV2Zbjm461R3ygdvvx+P/vqv4uS3\nvtnw2N37juOLtx6oOTZqReuSbhTQObaszN4r8xncEREREa2NohdAWNV9dvP+HELdmFESUlealCgp\nIU31ItMW/RHcjY84+OhvPAfPunRrr5fSkjYGIVpl7sKG4ebjmybXbG3Ue5aSCIWANCF2Lz5ZOT4R\nRHMmLauuAU/KQVHaHdtzd9/X/hUAcPxrX2l47Ph0AUiM8JBG46LT+wEAwokCOsdSKMq64G4djEVg\ncEdEREQDwfM15OQJGAOE85MwMMgHhZpzSkFUKpjsQKlQvW2xXX/bTCJzVx/ceYGujEj44lkvxN/u\nfAWU5GXlMLEsAS0UhG4+KsM/ebLmfsqNyiBNvjPBnR3vofObfGBT8kNcP/1Q5f6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HAAAg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\n/D/f6sakZob7AQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12cde32d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# shift train predictions for plotting\n",
    "trainPredictPlot_mpm = np.empty_like(dataset)\n",
    "trainPredictPlot_mpm[:] = np.nan\n",
    "trainPredictPlot_mpm[look_back:len(trainPredict_mpm)+look_back] = trainPredict_mpm.ravel()\n",
    "\n",
    "# shift test predictions for plotting\n",
    "testPredictPlot_mpm = np.empty_like(dataset)\n",
    "testPredictPlot_mpm[:] = np.nan\n",
    "testPredictPlot_mpm[len(trainPredict_mpm)+(look_back*2):len(dataset)] = testPredict_mpm.ravel()\n",
    "\n",
    "# plot baseline and predictions\n",
    "plt.plot(scaler.inverse_transform(dataset))\n",
    "plt.plot(trainPredictPlot_mpm)\n",
    "plt.plot(testPredictPlot_mpm)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 49.28000093,  48.75000171,  48.04000103,  45.70999913,\n",
       "        44.25000098,  42.80000003,  44.89000131,  45.35000018])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testY_mpm[0][-8:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.reshape(testY_mpm[0][-8:],(1,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 49.28000093,  48.75000171,  48.04000103,  45.70999913,\n",
       "         44.25000098,  42.80000003,  44.89000131,  45.35000018]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 44.48768997]], dtype=float32)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_MPM.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sarthakdasadia/anaconda/lib/python2.7/site-packages/sklearn/preprocessing/data.py:374: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n",
      "  warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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fu3cPDh48UOgxzjuvNv/737JSqQcgOTmZqVNf46KLLqZLlxsL3O+5557m889X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\n/t9ACCGEEEKMGB0MNqO4UqWnFTcg22Og6Pth3R5FnVnax+WCbg7ovNw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96rWHd3CvLl60sW9JV/NoFNsOc07FM2oqXGsVc8B0BfdyegEQ6sYv1V53DGqtVNP19fVxcAmlVu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lal+1LNXHSx/XAL5fV2YhuQJnT+CjlT70AiaRc9fqg7kLgSPIOWmnkYHcHEknNXtCEfEnMlCd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12c599650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot baseline and predictions\n",
    "plt.plot(scaler.inverse_transform(dataset), label= \"Actual Data\")\n",
    "plt.plot(trainPredictPlot_mpm, label= \"Predicted Training Data\")\n",
    "plt.plot(testPredictPlot_mpm, label= \"Predicted Test Data\")\n",
    "plt.xlabel(\"No of weeks since 1983-04-03\")\n",
    "plt.ylabel(\"WTI Oil price in USD / bbl\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
